System and method for mobility enhanced predictive measurements based on artificial intelligence / machine learning

Abstract

Systems and methods for using various artificial intelligence (AI) / machine learning (ML) models with respect to various mobility aspects are described herein. Generation and use of L3 beam level measurement predictions, L3 cell level measurement predictions, L1 measurement predictions, network-based timing advance (TA) value predictions, and UE-based TA value predictions are discussed using corresponding ML models. Various examples of inputs that can be used with respect to these ML models are discussed. Various of these predictions are discussed to be used within mobility environments including Layer 3-based handover, Layer 1 / Layer 2 triggered mobility (LTM), and Conditional Handover (CHO).

Description

TECHNICAL FIELD

[0001] This application relates generally to wireless communication systems, including wireless communication systems capable of performing measurements and / or timing advance (TA) prediction. BACKGROUND

[0002] Wireless mobile communication technology uses various standards and protocols to transmit data between base stations and wireless communication devices. For example, wireless communication system standards and protocols can include, for example, 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) (e.g., 4G), 3GPP New Radio (NR) (e.g., 5G), and Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (commonly referred to as Wi-Fi ® ).

[0003] As contemplated by 3GPP, different wireless communication system standards and protocols can use various radio access networks (RANs) for communication between base stations (which can also be referred to as RAN nodes, network nodes, or simply nodes) of the RAN and wireless communication devices referred to as user equipment (UE). A 3GPP RAN can include, for example, a Global System for Mobile Communications (GSM), Enhanced Data Rates for GSM Evolution (EDGE) RAN (GERAN), Universal Terrestrial Radio Access Network (UTRAN), Evolved Universal Terrestrial Radio Access Network (E-UTRAN), and / or Next Generation Radio Access Network (NG-RAN).

[0004] Each RAN can use one or more radio access technologies (RATs) to perform communication between base stations and UEs. For example, a GERAN implements GSM and / or EDGE RAT, a UTRAN implements Universal Mobile Telecommunications System (UMTS) RAT or other 3GPP RAT, an E-UTRAN implements LTE RAT (sometimes referred to simply as LTE), and an NG-RAN implements NR RAT (which is sometimes referred to herein as 5G RAT, 5G NR RAT, or simply NR). In certain deployments, an E-UTRAN can also implement NR RAT. In certain deployments, an NG-RAN can also implement LTE RAT.

[0005] A base station used by a RAN can correspond to that RAN. One example of an E-UTRAN base station is an Evolved Universal Terrestrial Radio Access Network (E-UTRAN) Node B (also commonly denoted as an evolved Node B, enhanced Node B, eNodeB, or eNB). One example of an NG-RAN base station is a Next Generation Node B (sometimes also referred to as a gNode B or gNB).

[0006] The RAN, through its connection to the core network (CN), also provides connectivity to external entities. For example, E-UTRAN can utilize an Evolved Packet Core (EPC), while NG-RAN can utilize a 5G Core Network (5GC). BRIEF DESCRIPTION OF DRAWINGS

[0007] To easily identify discussions of any particular element or act, one or more of the highest three digits of a figure number are often omitted in the following patent disclosure.

[0008] FIG. 1 An example framework for using AI and / or ML in the context of a wireless communication system is illustrated.

[0009] FIG. 2 A L3 measurement framework that can be used in a wireless communication system in accordance with the implementations discussed herein is illustrated.

[0010] FIG. 3 A flowchart for a UE-side procedure for L3 measurement prediction as between a UE and a network in accordance with the implementations herein is illustrated.

[0011] FIG. 4 A flowchart for a bilateral procedure for L3 measurement prediction as between a UE and a network in accordance with the implementations herein is illustrated.

[0012] FIG. 5A A first mechanism for temporal prediction of L3 cell-level measurements in accordance with the implementations discussed herein is illustrated.

[0013] FIG. 5B A second mechanism for temporal prediction of L3 cell-level measurements in accordance with the implementations discussed herein is illustrated.

[0014] FIG. 5C A third mechanism for temporal prediction of L3 cell-level measurements in accordance with the implementations discussed herein is illustrated.

[0015] FIG. 5D A fourth mechanism for temporal prediction of L3 cell-level measurements in accordance with the implementations discussed herein is illustrated.

[0016] FIG. 6A A first mechanism for temporal prediction of L3 beam-level measurements in accordance with the implementations discussed herein is illustrated.

[0017] FIG. 6B A second mechanism for temporal prediction of L3 beam-level measurements in accordance with the implementations discussed herein is illustrated.

[0018] FIG. 6CA third mechanism for temporal prediction of L3 beam level measurements according to the embodiments discussed herein is illustrated.

[0019] FIG. 7 A mechanism for spatial prediction of L3 beam level measurements according to the embodiments discussed herein is illustrated.

[0020] FIG. 8 A flow diagram of an LTM procedure between a UE and a base station of a network according to the embodiments discussed herein is illustrated.

[0021] FIG. 9 A diagram showing operation of an RSTD based TA mechanism as between a UE, a source cell, and a target cell according to the embodiments discussed herein is illustrated.

[0022] FIG. 10 A flow diagram of a UE side procedure for LI and / or TA measurement prediction as between a UE and a network according to the embodiments herein is illustrated.

[0023] FIG. 11 A flow diagram of a bilateral procedure for LI and / or TA measurement prediction as between a UE and a network according to the embodiments herein is illustrated.

[0024] FIG. 12 A mechanism for temporal prediction of LI measurements according to the embodiments discussed herein is illustrated.

[0025] FIG. 13 A mechanism for spatial prediction of LI measurements according to the embodiments discussed herein is illustrated.

[0026] FIG. 14 A diagram illustrating an example case for various TAs between a UE and each of a first cell, a second cell, and a third cell 1408 is illustrated.

[0027] FIG. 15 A mechanism for temporal prediction of RSTD based TA measurements according to the embodiments discussed herein is illustrated.

[0028] FIG. 16 A mechanism for spatial prediction of RSTD based TA measurements according to the embodiments discussed herein is illustrated.

[0029] FIG. 17A and FIG. 17B Together illustrate a flow diagram for implementing prediction of early TA in a system comprising a UE, a source base station in communication with the UE on a serving cell, a first target base station having a first target cell, a second target base station having a second target cell, and a server according to the embodiments discussed herein.

[0030] FIG. 18A and FIG. 18B together illustrate a flow diagram for conditional handover that can be used in some wireless communication systems.

[0031] FIG. 19 illustrates a flow diagram for a CHO procedure using a UE, a source base station in communication with the UE on a serving cell, a first target base station having a first target cell, a second target base station having a second target cell, and a server, in accordance with implementations discussed herein.

[0032] FIG. 20 illustrates a method of a UE, in accordance with implementations discussed herein.

[0033] FIG. 21 illustrates a method of a RAN, in accordance with implementations discussed herein.

[0034] FIG. 22 illustrates a method of a UE, in accordance with implementations discussed herein.

[0035] FIG. 23 illustrates a method of a UE, in accordance with implementations discussed herein.

[0036] FIG. 24 illustrates a method of a UE, in accordance with implementations discussed herein.

[0037] FIG. 25 illustrates a method of a RAN, in accordance with implementations discussed herein.

[0038] FIG. 26 illustrates a method of a UE, in accordance with implementations discussed herein.

[0039] FIG. 27 illustrates a method of a UE, in accordance with implementations discussed herein.

[0040] FIG. 28 illustrates a method of a source base station of a RAN, in accordance with implementations discussed herein.

[0041] FIG. 29 illustrates a method of a UE, in accordance with implementations discussed herein.

[0042] FIG. 30 illustrates a method of a source base station of a RAN, in accordance with implementations discussed herein.

[0043] FIG. 31 illustrates a method of a UE, in accordance with implementations discussed herein.

[0044] FIG. 32An example architecture of a wireless communication system in accordance with the embodiments disclosed herein is illustrated.

[0045] FIG. 33 A system for performing signaling between a wireless device and a network device in accordance with the embodiments disclosed herein is illustrated. DETAILED DESCRIPTION

[0046] Various embodiments are described in terms of a UE. However, the reference to a UE is provided for illustrative purposes only. Example embodiments can be used with any electronic component that can establish a connection with a network and is configured with hardware, software, and / or firmware for exchanging information and data with the network. Thus, a UE as described herein is used to represent any appropriate electronic component.

[0047] Framework for artificial intelligence / machine learning in wireless communication systems FIG. 1 An example framework 100 for using artificial intelligence (AI) and / or machine learning (ML) in the context of a wireless communication system is illustrated. The discussion herein relates to the use of AI / ML models (sometimes referred to herein simply as “models”).

[0048] The framework 100 includes data collection functionality 102, model training functionality 104, management functionality 106, inference / prediction functionality 108, and model storage functionality 110.

[0049] As shown, the data collection functionality 102 can provide training data 112 to the model training functionality 104, can provide monitoring data 114 to the management functionality 106, and / or can provide inference / prediction data 116 to the inference / prediction functionality 108. The model training functionality 104 can provide trained / updated model signaling 124 to the model storage functionality 110. The management functionality 106 can provide performance feedback / retraining request signaling 122 to the model training functionality 104, can provide model transfer / delivery request signaling 128 to the model storage functionality 110, and / or can provide selection / deactivation / switching / fallback signaling 120 to the inference / prediction functionality 108. The inference / prediction functionality 108 can provide output monitoring signaling 118 to the management functionality 106. The model storage functionality 110 can provide model transfer / delivery signaling 126 to the inference / prediction functionality 108.

[0050] In the framework 100, an AI / ML model can be trained at the model training functionality 104 based on training data 112 received from the data collection functionality 102. Once trained, the model can be provided to the model storage functionality 110.

[0051] When a model is to be used, it is provided from the model storage functionality 110 to the inference / prediction functionality 108. The data collection functionality 102 can also provide inference / prediction data 116 (e.g., input data) to the inference / prediction functionality 108. The inference / prediction functionality 108 can then make an inference by applying the inference / prediction data 116 to the model. The inference can be reported to functionality external to the framework 100 for further use.

[0052] The management functionality 106 manages the overall operation of the framework 100. Management decisions can be based on monitoring data 114 received at the management functionality 106 from the data collection functionality 102 and / or output monitoring signaling 118 received from the inference / prediction functionality 108. The management functionality 106 can for example provide training data 112 to the model training functionality 104 to inform the model training functionality 104 about the performance of a trained model and / or to request retraining of a current model. The management functionality 106 can for example provide model transfer / delivery request signaling 128 to the model storage functionality 110 to control transfer to the inference / prediction functionality 108 and use of the model at the inference / prediction functionality. The management functionality 106 can for example control the inference / prediction functionality 108 by selection / activation / deactivation / switching / fallback signaling 120 to indicate a model to be used and / or a method of using a current model, etc.

[0053] Use cases for artificial intelligence / machine learning in wireless communication systems With respect to wireless communication system considerations, various use cases have been identified for studying useful applications of AI / ML models for categories related to physical layer (PHY layer) considerations. One such case is studying the use of AI / ML models in a channel state information (CSI) feedback environment with the goal of enabling CSI feedback enhancement. For example, CSI time prediction using AI / ML models can be considered.

[0054] Another such case relates to beam management considerations. For example, layer 1 (LI) beam time / space prediction using AI / ML models can be considered.

[0055] Yet another such case relates to positioning accuracy enhancement that can be enabled by using AI / ML models.

[0056] There are various possible levels of UE / base station cooperation possible with respect to AI / ML usage within a wireless communication system. For example, in some cases, there can be no cooperation between the UE and the base station with respect to the usage of ML models. In other cases, there can be signaling-based cooperation between the UE and the base station, but no transfer of ML models between the base station and the UE (such cases can use, for example, assistance information for ML model selection purposes). In other cases, there can be signaling-based cooperation between the UE and the base station that includes transfer of ML models as used between the UE and the base station. At least some of the embodiments discussed herein are applicable to, for example, the signaling-based cooperation cases (with or without model transfer).

[0057] Proposals for wireless communication systems can involve AI / ML enhanced mobility cases. These cases can be divided into various sub-topics. For example, a first such sub-topic can be with respect to AI / ML based radio resource management (RRM) predictions, such as predictions of future Ll and / or layer 3 (L3) measurements based on historical measurements. In such cases, it can be intended to reduce UE measurement effort and / or reduce latency of triggering measurement events.

[0058] In another example, another such sub-topic can be with respect to AI / ML based target cell selection, such as with respect to predictions and informing the network of which cell and / or beam to switch to and / or when to switch. In such cases, it can be intended to allow the UE to not report all of its local useful observations with respect to handover to the network, thereby helping the UE to stay within given power and / or memory and / or privacy constraints.

[0059] In another example, another such sub-topic can be with respect to AI / ML based failure avoidance, such as with respect to predictions and informing the network of future possible radio link failure (RLF) / handover failure (HOF). In such cases, it can be intended to enable the network to proactively avoid RLFs, rather than reacting only after an RLF has occurred (according to some existing passive mechanisms).

[0060] Other use cases of beneficial applications of AI / ML include, but are not limited to, AI / ML based UE trajectory prediction, AI / ML based discontinuous reception (DRX) adaptation, AI / ML based slicing / QoE mechanisms, and / or AI / ML based cell reselection mechanisms.

[0061] Thus, it can be seen that there are multiple proposals to study AI / ML based mobility enhancements. In this document, details of various embodiments for such AI / ML based mobility enhancements are discussed.

[0062] In some embodiments herein, a UE can use the ML model trained at the UE based on the mobility and mobility related information of the UE. In some cases, the UE can inform the network of its prediction of the best target cell and / or beam for regular HO based on its use of the ML model. In some cases, the UE can inform the network of its prediction of the list of candidate cells for conditional handover (CHO) based on its use of the ML model. In some cases, the UE can be able to predict an upcoming RLF and inform the network in advance based on its use of the ML model.

[0063] Example L3 measurement framework FIG. 2 An L3 measurement framework 200 that can be used in a wireless communication system is illustrated in accordance with embodiments discussed herein. The L3 measurement framework 200 can be, for example, a measurement framework used at a UE of a NR wireless communication system.

[0064] Initially, the results sensed from each of the plurality of monitored gNB beam are subjected to a LI beam filter 202. This means that, for a number K of beams, each beam (from 1 to K ) can first be processed with a LI filter. As shown, the specific implementation of the LI filtering can be UE / UE type specific / manufacturer specific. Then, as shown, the per-beam results of the LI filtering go into two different stages: a L3 cell level measurement stage 204 and a L3 beam level measurement stage 206.

[0065] The L3 cell level measurement stage 204 is illustrated in the upper right portion of FIG. 2 . In the L3 cell level measurement stage 204, the per-beam LI filtered results are first combined 208 into one cell level value by linear averaging. This cell level value can then be passed to a corresponding L3 filter 210 to generate an output. When the output passes certain reporting criteria 212 at the UE, the output can be reported. As shown, the parameters used for / during this process can be configured to the UE by the network (e.g., via radio resource control (RRC) configuration).

[0066] The L3 beam level measurement stage 206 is illustrated in the lower right portion of FIG. 2 . In the L3 beam level measurement stage 206, the per-beam LI filtered results are each subjected to a per-beam L3 beam filter 214, and the UE can then select 216 the qualified beams (e.g., the X beams where X ≤ K) for output. As shown, parameters used for / during the procedure can be configured to the UE by the network (e.g., via RRC configuration).

[0067] In some wireless communication systems, L3 measurements can be configured to occur on a measurement object basis, where the measurement object is configured per frequency (rather than per cell). Thus, in such cases, if L3 beam reporting is configured (e.g., in an information element (IE)), the UE will apply the same measurement configuration to all cells using that same frequency (e.g., the UE will generate and transmit L3 beam measurement reports for all cells using that same frequency), even for such cells with poor cell quality. reportConfigNR

[0068] Correspondingly, in some wireless communication systems, measurement reporting can use a large amount of signaling overhead to implement reporting. For example, even in the case of measurement reporting for one single neighbor cell, up to 3572 bits of overhead can be used, as multiple measurement quantities (e.g., reference signal received power (RSRP) / reference signal received quality (RSRQ) / signal to interference plus noise ratio (SINR)) and multiple reference signal types (e.g., synchronization signal block (SSB) / channel state information reference signal (CSI-RS)) can be reported (and further note that in such cases, up to 64 SSBs / CSI-RSs can be configured for reporting).

[0069] Overview of prediction for L3 cell level measurements and L3 beam level measurements Thus, embodiments discussed herein can relate to solutions related to using predicted L3 cell level measurements and / or predicted L3 beam level measurements. Benefits resulting from using such L3 measurement predictions can include, for example, an overall reduction in UE measurement reporting effort corresponding to L3 measurement related cases. For example, a UE can perform L3 measurements on (e.g., only) a number N of top cells. Then, the UE can perform predictions (rather than actual measurements) on other cells (and can only return to perform actual L3 measurements of these cells when its prediction is that the cell has entered the top N number of cells).

[0070] Another benefit resulting from using L3 measurement predictions can be a measurement event time to trigger (TTT) reduction, which can correspondingly reduce UE HO latency, for example. This can be achieved by configuring measurement events to trigger based on predicted L3 cell measurements rather than waiting for corresponding actual L3 cell measurements.

[0071] ​Another benefit that can stem from using L3 measurement prediction can be a reduction in the use of measurement gaps and / or the use of measurement gaps of a relatively reduced duration. A UE can perform L3 measurement prediction with respect to one or more cells (e.g., inter-frequency cells) rather than implementing measurement gaps to implement actual L3 measurements of those cells. Such a reduction in the use of measurement gaps can allow the UE to use more channel resources for data transmission (which can be particularly useful in cases where the UE has pending data for transmission).

[0072] Accordingly, embodiments herein discuss various aspects with respect to L3 measurement prediction. A first aspect is an overall procedure between a base station and a UE for using L3 measurement prediction. Another such aspect relates to mechanisms for performing an inference of a predicted L3 cell measurement. For instance, cases are discussed of using each of a UE-side model and a bilateral model to perform each of L3 cell-level measurement prediction and L3 beam-level measurement prediction (note that a network-side model can have the UE report local datasets to the network for model training). Yet another such aspect relates to performance monitoring at the base station and / or the UE with respect to the results of the ML model (as well as other corresponding lifecycle monitoring (LCM) aspects of the ML model). Moreover, such aspects discussed include assistance information that can be communicated between the UE and the network / base station in these environments.

[0073] UE-side procedures for L3 measurement prediction Details are accordingly discussed herein with respect to the generation and use of AI / ML-based L3 measurement prediction for mobility enhancement at a UE. FIG. 3 A flow diagram 300 is illustrated for a UE-side procedure for L3 measurement prediction as between a UE 302 and a network 304 in accordance with embodiments herein is illustrated. Note that in some embodiments, the UE-side procedure for L3 measurement prediction can also incorporate the use of a UE server 306, as will be discussed.

[0074] As FIG. 3 The UE-side procedure for L3 measurement prediction as illustrated can correspond to, for instance, cases of L3 cell-level measurement prediction and / or L3 beam-level measurement prediction.

[0075] Flowchart 300 begins with UE 302 generating and sending a UE capability report 308 to network 304. In some embodiments, UE capability report 308 can include one or more of the following: whether the UE supports L3 cell level time measurement prediction; whether the UE supports L3 beam level time measurement prediction; whether the UE supports L3 cell level spatial measurement prediction; whether the UE supports L3 beam level spatial measurement prediction; - a maximum number of historical samples / slots that can be used at / by the UE for prediction; a maximum number of prediction samples / slots that can be used at / by the UE; - a maximum number of parallel predictions that can be used at / by the UE.

[0076] Network 304 then provides a training configuration 310 to UE 302. Training configuration 310 can include one or more of the following: a type of ML model to be trained (e.g., a long short-term memory (LSTM) ML model type, a recurrent neural network (RNN) ML model type, etc.), layers to be trained and / or one or more specialized ML models to be used; a window length to be used with the ML model corresponding to history of measurements and / or predictions in time domain and / or spatial domain; and / or a number of parallel predictions that should be provided by UE 302.

[0077] UE 302 then performs data collection 312. In some embodiments, this process incorporates generating / training ML models at the UE using the collected data. In some embodiments, the UE provides the collected data to UE server 306 so that offline training 314 (e.g., generation of ML models) instead occurs at UE server 306, which then provides the ML models so generated back to UE 302.

[0078] UE 302 then transmits a notification message 316 to network 304. The contents of notification message 316 can inform network 304 which ML models (and, in at least some cases, model identifiers (IDs) corresponding to these ML models) are available at UE 302.

[0079] The contents of notification message 316 can inform network 304 of model suitability conditions, which can be used by network 304 to determine which ML model to use at UE 302. Note that the model suitability conditions can include, for example, usage scenario information (e.g., indoor / outdoor), antenna type information, channel type information, UE speed information (e.g., UE traveling less than 5 kilometers per hour (kmph)), UE height information (e.g., corresponding to UE movement in altitude), etc.

[0080] It is noted that the notification message 316 is provided, e.g., as part of a scheduling request (SR), as part of uplink assistance information (UAI), in a medium access control control element (MAC-CE), or in RRC messaging (e.g., in a RRCReconfigurationComplete message or a newly provisioned RRC message).

[0081] Based on the notification message 316, the network 304 can determine which ML model is to be activated at the UE 302, and can provide an activation message 318 to the UE 302 that commands the UE 302 to activate the selected ML model. The activation message 318 can be provided, e.g., as part of downlink control information (DCI), a MAC-CE, or RRC messaging.

[0082] It is noted that, in an alternative implementation, shown in FIG. 3B, the UE 302 can instead directly inform the network 304 which ML model it prefers to use or will use. In such a case, it is possible that the UE 302 determines its preferred / used model based on information such as the speed of the UE 302 and / or channel conditions experienced by the UE 302. FIG. 3

[0083] The UE 302 then continues to perform the inference / prediction 320 for L3 cell level measurements and / or L3 beam level measurements (as the case can be) according to the configuration of the network 304, as discussed. The inference / prediction 320 can be made based on actual measurements that have been made at the UE.

[0084] It is possible that the UE 302 is / had been configured (e.g., by the network 304) to trigger a measurement report 322 based on one or more of actual L3 measurements and / or predicted L3 measurements. The measurement report 322 can be transmitted once appropriate values of actual and / or predicted measurements have been determined at the UE. The measurement report 322 can report to the base station either / both of the real and / or predicted L3 cell level and / or beam level measurements (this can occur using, e.g., MeasurementResults messages).

[0085] It is conceivable that, with respect to the UE-side procedures, the UE 302 or the network 304 can perform performance monitoring 324 of the ML model (e.g., by comparing the predicted measurements with the corresponding actual measurements). Based on the results of the performance monitoring 324, the UE 302 or the network 304 can initiate LCM signaling 326 for model switching or model deactivation, which then results in model switching / model deactivation 328 at the UE. In the deactivation case, it is possible that the UE 302 and the network 304 then fall back to a non-AI / ML based measurement reporting solution.

[0086] Bilateral procedures for L3 measurement prediction FIG. 4 ​A flow diagram 400 for a bilateral procedure for L3 measurement prediction, such as between a UE 402 and a network 404, is illustrated in accordance with embodiments herein. Note that, in some embodiments, the UE-side procedure for measurement prediction can also incorporate use of a UE server 406.

[0087] The bilateral procedure for L3 measurement prediction, as shown, can correspond to cases of, for example, L3 cell-level prediction and / or L3 beam-level prediction. FIG. 3

[0088] The UE capability report 308, training configuration 310, data collection 312, and offline training 314 can all occur as described herein with respect to the UE-side procedure discussed with respect to FIG. 3

[0089] Once the ML model exists at the UE 402, model transfer 408 occurs during which the UE 402 communicates the ML model to the network 404. The model transfer signaling can be RRC-based or data radio bearer (DRB)-based.

[0090] The UE 402 can then generate actual L3 cell-level and / or beam-level measurements (e.g.), and transmit a measurement report 410 to the network 404 with these actual measurements.

[0091] The network 404 can then apply these actual measurements with the previously received ML model in order to perform L3 cell-level and / or L3 beam-level measurement prediction 412, as appropriate. In some embodiments, the detailed prediction method can depend on the specific implementation of the network 404.

[0092] It is further contemplated that performance monitoring 414 can occur at the network 404 (e.g., by comparing actual L3 cell-level and / or beam-level measurements received from the UE 402 to corresponding predicted L3 cell-level and / or beam-level measurements).

[0093] The network 404 can also be configured to trigger performance of ML model retraining based on its specific implementation, which can make this decision based at least in part on the results of the performance monitoring 414, for example. As part of triggering this retraining, the network 404 can provide a retraining configuration 416 to the UE that commands the retraining (and can provide one or more parameters for the UE to analyze / use as part of the ML model retraining procedure).

[0094] In response to the retraining configuration 416, the UE continues to perform data collection 312, offline training 314, and model transfer 408 again, in some embodiments, as previously described.

[0095] Example mechanisms for making L3 cell level measurement predictions ​​ In some embodiments, to provide a flexible tradeoff between UE measurement burden and mobility performance, the UE can be configured (e.g., via RRC signaling) for one of various alternative schemes of L3 cell level measurement prediction by an ML model (which can be referred to as a “measurement prediction model”).

[0096] In a first type of embodiment for L3 cell level measurement prediction, the possible L3 cell level measurement prediction corresponds to time prediction. For example, the measurement prediction model can be used to make a prediction of a future L3 cell level measurement based on current input to the measurement prediction model.

[0097] FIG. 5A A first mechanism 502 for time prediction of L3 cell level measurements is illustrated in accordance with embodiments discussed herein. One or more actual L3 cell level measurements 504 can be performed at the UE. The measurement prediction model can be configured to receive these one or more actual L3 cell level measurements 504 as input, and in response provide one or more predicted L3 cell level measurements 506 (where each of the one or more predicted L3 cell level measurements 506 corresponds to some later time).

[0098] FIG. 5B A second mechanism 508 for time prediction of L3 cell level measurements is illustrated in accordance with embodiments discussed herein. One or more actual LI beam level measurements can be made. In FIG. 5B In an example, the UE takes a first actual LI beam level measurement 510 of a first beam and a second actual LI beam level measurement 512 of a second beam.

[0099] The UE then uses the measurement prediction model to predict one or more predicted LI beam level measurements. In FIG. 5B In an example, the UE uses the first actual LI beam level measurement 510 and the second actual LI beam level measurement 512 with the measurement prediction model to generate a first predicted LI beam level measurement 514 and a second predicted LI beam level measurement 516.

[0100] The UE then performs a linear average 518 of the one or more predicted beam level measurements. In FIG. 5A In an example, the UE performs a linear average of the first predicted LI beam level measurement 514 and the second predicted LI beam level measurement 516.

[0101] The result of the linear averaging 518 is then filtered using L3 filtering 520 to generate one or more predicted L3 cell-level measurements 522. The L3 filtering 520 can occur according to L3 filter coefficients configured by the network (e.g., through RRC signaling), as shown. In some embodiments, the L3 filter coefficients that can be used for the L3 filtering are generated by the measurement prediction model (e.g., based on its receipt of the first actual L1 beam-level measurements 510 and the second actual L1 beam-level measurements 512).

[0102] FIG. 5C A third mechanism 524 for time prediction of L3 cell-level measurements according to embodiments discussed herein is illustrated. One or more actual L1 beam-level measurements can be made. In the example shown, the UE obtains a first actual L1 beam-level measurement 526 of a first beam and a second actual L1 beam-level measurement 528 of a second beam. FIG. 5C

[0103] The UE then performs linear averaging 530 on the one or more actual L1 beam-level measurements. In the example shown, the UE performs linear averaging 530 on the first actual L1 beam-level measurement 526 and the second actual L1 beam-level measurement 528. FIG. 5C

[0104] The result of this linear averaging (e.g., a derived actual cell-level L3 measurement 532 in FIG. 5C The derived actual cell-level L3 measurement 532 in

[0105] In such embodiments, the dwell / valid time that can be used for the prediction is also calculated by the measurement prediction model and provided as a result along with the one or more predicted L3 cell-level measurements 534.

[0106] FIG. 5D A fourth mechanism 536 for time prediction of L3 cell-level measurements according to embodiments discussed herein is illustrated. One or more actual L1 beam-level measurements can be made. In the example shown, the UE obtains one or more first actual L1 beam-level measurements 538 of a first beam and one or more second actual L1 beam-level measurements 540 of a second beam. FIG. 5D

[0107] The one or more actual L1 beam-level measurements (e.g., the one or more first actual L1 beam-level measurements 538 and the one or more second actual L1 beam-level measurements 540) and L3 coefficients 542 (e.g., RRC configured L3 coefficients) are provided to a measurement prediction model 544, which uses these items to generate one or more predicted L3 cell-level measurements 546.​​​

[0108] The use of L3 coefficients 542 in such cases can compensate for the fact that L1 measurements (such as the one or more first actual L1 beam level measurements 538 and the one or more second actual L1 beam level measurements 540) can not be as stable as corresponding L3 measurements (and in light of the recognition that in such cases the UE does not itself generate more stable actual L3 measurements in such cases).

[0109] In a second type of implementation for L3 cell level measurement prediction, L3 cell level measurement prediction corresponds to spatial prediction. For example, if base station deployment geometry and / or long term channel time statistics (e.g., correlation) are available, the UE can use a measurement prediction model to infer L3 measurements of neighbor cells based on nearby deployment (e.g., correlation information) of the current cell and L3 cell measurements of the current cell.

[0110] Note that configuration information (e.g., RRC configuration information) can be provided to command the UE to make L3 cell level measurement predictions with respect to use of a measurement prediction model. For example, the UE can be configured to generate L3 cell level measurement predictions with respect to a particular cell (e.g., a current serving cell and / or a neighbor cell).

[0111] As another example of using L3 cell level measurement predictions in accordance with configuration information, the UE can be configured to generate L3 cell level measurement predictions with respect to a particular frequency.

[0112] As another example of using L3 cell level measurement predictions in accordance with configuration information, the UE can be configured to generate L3 cell level measurement predictions based on one or more conditions.

[0113] In a first example of a condition for using predicted L3 cell level measurements, the UE can be configured to generate L3 cell level measurements for up to a number N of cells, where the UE generates actual L3 cell level measurements for a number M of cells previously known to have the strongest RSRP / RSRQ, and further generates predicted L3 cell level measurements for a remaining M N number of cells. In the event that a predicted L3 cell level measurement falls within N M the top M M number of measurements, then the UE will perform an actual L3 cell level measurement for a subsequent cell (and the last cell in the previous N M set is joined by an alternative use of a predicted

[0114] ​​​​In a second example of conditions for using predicted L3 cell level measurements, the configured RSRP / RSRQ / SINR thresholds can be compared to actual or predicted L3 cell level measurements. If the actual or predicted L3 cell level measurements for a cell are less than the threshold, the UE performs L3 cell level prediction for the subsequent cell. In a variation of this case, it can be possible for the base station to configure the UE to use separate thresholds for the use of actual L3 cell level measurements and predicted L3 cell level measurements.

[0115] In a third example of conditions for using predicted L3 cell level measurements, it can be possible to perform L3 cell level measurement prediction in cases of strong interference in a cell. For example, in cases where the interference in a cell is greater than a configured interference measurement threshold, prediction can be used.

[0116] In a fourth example of conditions for using predicted L3 cell level measurements, L3 cell level measurement prediction can be performed for all or some of the indicated inter- frequency measurements.

[0117] In a fifth example of conditions for using predicted L3 cell level measurements, L3 cell level measurement prediction can be performed if the measurement uses a measurement gap (e.g., for measurements of another frequency or another non-overlapping bandwidth part (BWP)).

[0118] In a sixth example of conditions for using predicted L3 cell level measurements, the use of L3 cell level measurement prediction can depend on the mobility level of the UE. For example, when the UE is moving at a very low speed, the UE can use L3 cell level measurement prediction.

[0119] As another example of using L3 cell level measurement prediction according to configuration information, the configuration information can indicate that the UE is allowed to autonomously / independently select neighbor cells and / or frequencies for which to generate actual L3 cell level measurements or predicted L3 cell level measurements.

[0120] As another example of using L3 cell level measurement prediction according to configuration information, the configuration information can indicate that the UE is to use L3 cell level measurement prediction for one or more indicated cells.

[0121] As another example of using L3 cell level measurement prediction according to configuration information, the configuration information can indicate that the UE is to use L3 cell level measurement prediction for one or more indicated frequencies.

[0122] Example mechanisms for making L3 beam level measurement predictions In some embodiments, to provide a flexible trade-off between UE measurement burden and mobility performance, the UE can be configured (e.g., via RRC signaling) for one of various alternative schemes of L3 beam level measurement prediction by an ML model (which can be referred to as a “measurement prediction model”).

[0123] In a first type of implementation for L3 beam level measurement prediction, the possible L3 beam level measurement prediction corresponds to temporal prediction. For example, a measurement prediction model can be used to make a prediction of future L3 beam level measurements based on current inputs to the measurement prediction model.

[0124] FIG. 6A A first mechanism 602 for temporal prediction of L3 beam level measurements is illustrated in accordance with implementations discussed herein. One or more actual L3 beam level measurements 604 can be performed at the UE. A measurement prediction model can be configured to receive these one or more actual L3 beam level measurements 604 and optimized L3 filter coefficients as inputs, and in response provide one or more predicted L3 beam level measurements 606 (where each of the one or more predicted L3 beam level measurements 606 corresponds to some later time).

[0125] In such implementations, the resident / effective time for prediction is also computed by the measurement prediction model and provided as a result along with the one or more predicted L3 beam level measurements 606.

[0126] FIG. 6B A second mechanism 608 for temporal prediction of L3 beam level measurements is illustrated in accordance with implementations discussed herein. One or more actual L1 beam level measurements can be made. In FIG. 6B In an example, the UE makes actual L1 beam level measurements 610 for a first beam.

[0127] The UE then uses a measurement prediction model to predict one or more predicted L1 beam level measurements. In FIG. 6B In an example, the UE uses the actual L1 beam level measurements 610 with a measurement prediction model to generate predicted L1 beam level measurements 612.

[0128] L3 filtering 614 is then used on the one or more predicted L1 beam level measurements 612 to generate one or more predicted L3 beam level measurements 616. The L3 filtering 614 can occur according to network configured (e.g., through RRC signaling) L3 filter coefficients, as illustrated.

[0129] FIG. 6C A third mechanism 618 for temporal prediction of L3 beam level measurements is illustrated in accordance with implementations discussed herein. One or more actual L1 beam level measurements can be made. In FIG. 6C In an example, the UE makes actual L1 beam level measurements 620 for a first beam.

[0130] One or more actual L1 beam level measurements 620 and L3 coefficients 622 (e.g., RRC configured L3 coefficients) are provided to a measurement prediction model 624, which uses these items to generate one or more predicted L3 beam level measurements 626.

[0131] The use of L3 coefficients 622 in such cases can compensate for the fact that actual L1 beam level measurements 620 can not be as stable as corresponding L3 measurements (and in light of the recognition that in such cases the UE does not itself generate more stable actual L3 measurements in such cases).

[0132] In a second type of implementation for L3 beam level measurement prediction, L3 beam level measurement prediction corresponds to spatial prediction. For example, a UE can use its understanding of applicable spatial channel statistics to predict / infer a (predicted) L3 beam level measurement for one beam based on actual or predicted L3 beam level measurements for its neighbor beams.

[0133] FIG. 7 A mechanism 700 for spatial prediction of L3 beam level measurements is illustrated in accordance with implementations discussed herein. FIG. 7 A spatial beam arrangement 702 is illustrated for each of a first beam (“Beam 1”), a second beam (“Beam 2”), and a third beam (“Beam 3”).

[0134] One or more actual L3 beam level measurements can be made. In FIG. 7 In the example of FIG. 7, the UE makes one or more first actual L3 beam level measurements 704 for the first beam (Beam 1) and one or more second actual L3 beam level measurements 706 for the third beam (Beam 3).

[0135] The one or more actual L3 beam level measurements (e.g., the one or more first actual L3 beam level measurements 704 and the one or more second actual L3 beam level measurements 706) are provided to a measurement prediction model 708, which uses these items to generate one or more predicted L3 beam level measurements 710 for the second beam (Beam 2, which is a neighbor beam to Beam 1 and Beam 3 as illustrated). Note that in some implementations, the measurement prediction model 708 can use applicable spatial channel statistics to generate the one or more predicted L3 beam level measurements 710.

[0136] Note that configuration information (e.g., RRC configuration information) can be provided to instruct the UE to make L3 beam level measurement predictions with respect to the use of a measurement prediction model. For example, the UE can be configured to generate L3 beam level measurement predictions for particular beams with respect to particular cells (e.g., a current serving cell and / or a neighbor cell).

[0137] As another example of using L3 beam level measurement prediction based on configuration information, the UE can be configured to generate L3 beam level measurement predictions based on one or more conditions.

[0138] In a first example of conditions for using predicted L3 beam level measurements, the UE can be configured to generate L3 beam level measurements for up to a number N of beams, where the UE generates actual L3 beam level measurements for a number M of beams previously known to have the strongest RSRP / RSRQ, and further generates predicted L3 beam level measurements for the remaining M N number of beams. In the case that the predicted L3 beam level measurement falls in the N M top set of measurements, then the UE will perform actual L3 beam level measurements for subsequent beams (and the last beam in the previous M M set is joined by the alternative use of predicted N M set.

[0139] In a second example of conditions for using predicted L3 beam level measurements, a configured RSRP / RSRQ / SINR threshold can be compared to actual or predicted L3 beam level measurements. If the actual or predicted L3 beam level measurement for a cell is less than the threshold, then the UE performs L3 beam level prediction for the beams. In a variation of this case, the base station can configure the UE to use separate thresholds for the use of actual L3 beam level measurements and predicted L3 beam level measurements.

[0140] In a third example of conditions for using predicted L3 beam level measurements, two thresholds can be used. A first configured RSRP threshold can be compared to the actual / predicted L3 cell level measurement for a cell. If the actual or predicted L3 cell level measurement for the cell is greater than the threshold, then the UE can perform L3 beam level measurement prediction for one or more beams in that cell. A second configured RSRP / RSRQ threshold can then be compared to the actual or predicted L3 beam level measurements for the beams of that cell to determine whether beams in subsequent cells use actual L3 beam level measurements or predicted L3 beam level measurements. In a variation of this case, the base station can configure the UE to use separate thresholds for the use of actual measurements and predicted measurements.

[0141] In a fourth example of conditions for using predicted L3 beam level measurements, L3 beam level measurement prediction can be performed for all or some of the indicated inter-frequency measurements.

[0142] ​​​​In a fifth example of using conditions for using predicted L3 beam level measurements, L3 beam level measurement prediction can be performed if the measurement uses a measurement gap (e.g., for a measurement of another frequency or another non-overlapping BWP).

[0143] In a sixth example of using conditions for using predicted L3 beam level measurements, the use of L3 beam level measurement prediction can depend on a mobility level of the UE. For example, when the UE is moving at a very low speed, the UE can use L3 beam level measurement prediction.

[0144] As another example of using L3 beam level measurement prediction according to configuration information, the configuration information can indicate neighbor cells and / or frequencies that the UE is allowed to autonomously / independently select to generate actual L3 beam level measurements or predicted L3 beam level measurements.

[0145] Implementations for reporting actual and / or predicted L3 measurements It is contemplated that periodic and event triggered L3 measurement reporting can be supported.

[0146] In some embodiments, for periodic measurement reporting, the UE can be configured by the network with two periodicities in one reporting configuration. A first periodicity of the two periodicities can be a predicted measurement periodicity that indicates how often the UE should perform and / or report AI / ML based L3 measurement prediction. A second periodicity of the two periodicities can be an actual measurement periodicity that indicates how often the UE should perform and / or report actual L3 measurements. In some such embodiments, the predicted measurement periodicity can be less than the actual measurement periodicity, such that the UE can use L3 measurement prediction most of the time (e.g., to reduce power), while still occasionally reporting actual L3 measurements to provide more accurate updates / information that can be used for model monitoring.

[0147] In some embodiments, for event triggered reporting, the UE can be configured to use actual and / or predicted L3 measurements to trigger measurement reporting events (e.g., events Al-A6 as can be understood in a 3GPP NR wireless communication system). In a first case, only actual L3 cell level measurements can trigger the measurement reporting events.

[0148] In a second case of event triggered reporting, either actual or predicted L3 measurements can trigger the measurement reporting events. In this second case, the UE can also be configured to trigger the measurement reporting based on the predicted measurement L3 measurements when a confidence level for the predicted measurements is greater than a threshold.

[0149] In either case, once a measurement reporting event is triggered, the UE can report available predicted and / or actual L3 cell-level and / or beam-level measurements as part of the measurement report. Within the measurement report, the UE can indicate which cell and / or beam measurements are predictive measurements and / or which cell and / or beam measurements are actual measurements. In at least some embodiments, the UE can also indicate, in the case of predictive measurements, a reliability probability or confidence level of any predictive measurements in the measurement report.

[0150] For both periodic measurement reporting and event-triggered measurement reporting cases, the UE can first report actual cell and / or beam measurements, and then report predicted cell and / or beam measurements in an order that first provides a number of measurements from high to low (e.g., RSRP), and then provides a confidence level from high to low (e.g., corresponding to any predictive measurements).

[0151] In some embodiments, the total number of parallel predictions can not exceed the capability of the UE. In one such embodiment, the base station implementation can be relied upon to select good conditions with respect to the number of parallel predictions. In another such embodiment, the UE can be allowed to drop measurements in the following order: first drop beam-level measurements, then drop any beam-level and / or cell-level measurements with poor radio conditions, and then drop any predictive beam and / or cell measurements corresponding to poor confidence levels.

[0152] Model monitoring and LCM for L3 measurement prediction Envisioning with respect to UE-side procedures for L3 measurement prediction, model monitoring (e.g., monitoring of performance of the ML model) can be performed at the UE and / or at the base station. In the case of model monitoring performed at the base station, the procedure can depend on the base station implementation.

[0153] In the case of model monitoring performed at the UE, in some such embodiments, the model monitoring metric used by the UE can be the error between the predicted L3 cell / beam-level measurements and the corresponding actual L3 cell / beam-level measurements. For example, the mean squared error (MSE) between some of the predicted L3 cell-level / beam-level measurements and the corresponding actual L3 cell-level / beam-level measurements can be used (and note that using the MSE metric in this way can be an example of a “confidence level” as discussed herein). For monitoring purposes, the UE can perform both the predicted L3 cell-level / beam-level measurements and the corresponding actual L3 cell-level / beam-level measurements for a small set of cells and / or beams, as appropriate.

[0154] It is conceivable that the ML model in use can be switched from time to time (e.g., a different ML model will be selected for use and / or the use of the ML model for making predictions can be paused or stopped). This can occur, for example, based on the results of model monitoring, as will be described. The UE can be configured to perform a UE-initiated model switch or a network-initiated model switch.

[0155] In the case of a UE-initiated switch, the UE can be configured with conditions on model metrics (e.g., confidence level) and UE behavior. For example, the UE can be configured with MSE with a 0.01 threshold. When MSE is greater than 0.01, the UE can be configured to fall back to using actual measurements.

[0156] In the case of a network-initiated switch, the UE can report model monitoring metrics (e.g., confidence level) and can wait for base station LCM signaling in response (e.g., an instruction to stop using the ML model and / or start using a different ML model). The UE can report model monitoring metrics via UAI or MAC-CE.

[0157] With respect to a bilateral procedure for L3 measurement prediction, model monitoring can be performed at the base station side. This monitoring can be implementation-specific to the base station.

[0158] With respect to a UE-side and / or bilateral procedure for L3 measurement prediction using base station monitoring of the ML model, it is possible that the UE can be configured to provide information to the base station to help the base station perform the monitoring. Such information can include additional temporal information, such as timestamps for the predictions, and timestamps for the corresponding actual measurements. Such information can also / alternatively include additional spatial information, such as actual positioning of the UE, actual movement orientation of the UE, changes to the movement orientation of the UE, and / or delta direction (difference) that can be compared to the predictions.

[0159] Assistance information for L3 measurement prediction The assistance information used with respect to L3 measurement prediction can include assistance information communicated from the UE to the base station. Further, the assistance information used with respect to L3 measurement prediction can also / alternatively include assistance information communicated from the base station to the UE.

[0160] The assistance information communicated by the UE to the base station (e.g., to the network) can include, but is not limited to: predicted best L3 filter coefficients; predicted best measurement reporting event type; predicted best time-to-trigger (TTT) for a MR event; predicted best threshold value for a measurement reporting event; one or more suggested cells for actual measurements; one or more suggested beams for actual measurements; and / or suggested T304 timer value.

[0161] A notification message (e.g., to identify one or more ML models at the UE to the network) can be used to communicate the assistance information from the UE to the base station.

[0162] The assistance information transmitted by the base station (e.g., network) to the UE can include, but is not limited to: information about the nearby base station deployment geometry; long-term statistics of time correlation; and / or long-term statistics of inter-cell and / or inter-beam correlation.

[0163] A downlink (DL) message can be used to transmit the assistance information from the base station to the UE. The message can be a MAC-CE or an RRC message (e.g., RRCReconfigurationComplete message or a new RRC message).

[0164] Implementations for L1-L2 triggered mobility L1-L2 triggered mobility (LTM) procedures can be used in some wireless systems (e.g., such as NR Release 18 (Rel-18)). LTM refers to a UE mobility mechanism in the system that is based on L1 measurements at the UE / L1 measurement reports from the UE (rather than, e.g., L3 measurements at the UE / L3 measurement reports from the UE).

[0165] FIG. 8 A flowchart 800 of an LTM procedure between a UE 802 and a base station 804 of a network according to the embodiments discussed herein is illustrated. The LTM procedure represented by the flowchart 800 anticipates an LTM preparation phase 806, an early synchronization phase 808, an LTM execution phase 810, and an LTM completion phase 812.

[0166] The LTM preparation phase 806 anticipates that the UE 802 is in an RRC connected mode 814 and transmits a measurement report 816 to the base station 804. Based on the measurement report 816, the base station 804 performs LTM candidate preparation 818 (e.g., the base station 804 selects one or more of the cells from the measurement report 816 to configure as LTM candidates). The base station 804 then transmits a RRCReconfiguration message 820 to the UE 802 with LTE candidate configuration information (information about the one or more LTM candidates selected by the base station 804). The UE 802 responds to the RRCReconfigurationComplete message 820 with a RRCReconfiguration message 822.

[0167] Note that the LTM candidate cell configuration can be added, modified, and / or released by the network via RRC signaling. The cell configuration for each LTM candidate can be provided as a delta configuration with respect to a reference configuration.

[0168] The early synchronization phase 808 of the LTM procedure anticipates that the UE 802 performs DL / UL synchronization 824 with the candidate cells in preparation for potentially performing mobility to one or more of those candidate cells during the LTM execution phase 810.

[0169] The LTM execution phase 810 of the LTM process anticipates using L1 beam-level measurements (e.g., RSRP / RSRQ) regarding reference signals (e.g., SSB or CSI-RS) on those beams. L1 measurement information is provided by UE 802 to base station 804 in an L1 measurement report 826. Base station 804 can be configured to make an LTM decision 828 based on the information in the L1 measurement report 826. The LTM decision 828 can be a decision to instruct UE 802 on mobility to candidate cells based on the information in the L1 measurement report 826 (as previously stated in...). RRCReconfiguration (Configured for UE 802 in message 820). Corresponding to LTM decision 828, base station 804 transmits cell handover command 830 to UE 802 via MAC-CE. This cell handover command indicates / identifies the selected LTM candidate cell configuration for the selected candidate cell.

[0170] Then, the UE leaves the source cell 832 and applies the identified configuration to the selected candidate / target cells. The UE 802 further initiates a Random Access Channel (RACH) procedure 834 with these cells.

[0171] LTM Completion 836 of LTM Completion Phase 812 corresponds to the end of the LTM process, at which point the UE has completed mobility to the indicated candidate cell.

[0172] The LTM procedure can support candidate target cell TA acquisition via Early Timing Advance (TA) acquisition or RSTD-based TA acquisition. For example, if the TA of the candidate target cell is indicated in the MAC-CE that triggers HO / mobility, RACH-free communication between UE 802 and the candidate cell of the LTM procedure may be allowed / enabled.

[0173] The LTM procedure also enables fault handling. The UE can start an LTM supervisory timer upon receiving a cell handover command. Upon successful LTM cell handover, the UE stops the timer. If the timer expires instead, the UE considers the LTM cell handover a failure and can initiate an RRC connection re-establishment procedure to return to the previous serving cell.

[0174] Implementations for timing advance acquisition on target cell In some wireless systems, TA acquisition on a target cell can be achieved through one of a variety of possible TA acquisition mechanisms.

[0175] The first possible TA acquisition mechanism is the network-based TA acquisition mechanism. This network-based TA acquisition mechanism may also be referred to as the "early TA acquisition mechanism" in this paper. Under some of these mechanisms, the network estimates the UE's TA value and maintains those TA values ​​at the UE.

[0176] First, it's important to note that the network ensures that the cell transmission times used by the network's cells are synchronized on the network side. Then, under the early TA acquisition mechanism, the UE transmits preambles to candidate target cells from the contention-free random access (CFRA) resources known to the network side for TA estimation. Based on the reception times of these preambles at each cell (which occur due to the varying distances between the UE and the cells), the network determines (and communicates to / maintains at the UE) one or more TAs that the UE will use for the corresponding cell.

[0177] After transmitting the preamble, the UE can return to its source cell (for example, when the preamble is transmitted for the purpose of network determination of the TA value maintained by the network, it may not be necessary to receive the random access response (RAR) to the preamble).

[0178] Regarding this network-maintained TA value / early TA acquisition situation, the source cell can instruct the UE to perform the early TA acquisition process by triggering the UE to execute the Physical Downlink Control Channel (PDCCH) command sent to the target cell via RACH / preamble.

[0179] The second possible TA acquisition mechanism is the UE-based TA acquisition mechanism. This UE-based TA acquisition mechanism may also be referred to in this paper as the "TA mechanism based on Received Signal Time Difference (RSTD)".

[0180] FIG. 9 Figure 900 illustrates the operation of an RSTD-based TA mechanism between UE 902, source cell 904, and target cell 906, according to the implementation scheme discussed herein. First, it should be noted that the network ensures that the source cell transmission time 908 and the target cell transmission time 910 are synchronized on the network side, as shown in the figure. Under the RSTD-based TA mechanism, UE 902 can estimate and maintain the TA for each candidate target cell (such as target cell 906) and report this TA to the network. In such an environment, the UE may derive the TA of target cell 906 based on / by considering both the RSTD between the current serving cell and the target serving cell, and the known TA value of the current serving cell.

[0181] For example, such as FIG. 9As shown, UE 902 can determine the RSTD 916 between the source cell reception time 912 of source cell 904 and the target cell reception time 914 of target cell 906. UE 902 can then multiply the RSTD 916 between source cell 904 and target cell 906 by 2 (to take into account uplink (UL) and DL aspects regarding TA usage). UE 902 then adds this value to the known TA value of source cell 904 to obtain the TA value of target cell 906 (note that in some cases, this value can be negative).

[0182] Overview of prediction for L1 beam level measurements This paper discusses the details of generating and using AI / ML-based LTM enhancements at the UE. As already discussed, LTM decisions can be based on L1 measurements, which may involve one or more potential considerations. First, L1 measurements may be relatively less stable than their corresponding L3 measurements. Therefore, in some cases, using L1 measurements (compared to L3 measurements) for mobility may lead to frequent cell handover / ping-pong handover (HO). Therefore, the implementation scheme in this paper relates to UE-side AI / ML for L1 measurement prediction, which uses a robust HO decision process that minimizes the likelihood of such problems.

[0183] Another potential consideration is that performing L1 measurements and corresponding reports on multiple candidate cells incurs an additional burden on the UE. Regarding this issue, some implementations in this paper for UE-side and network-side L1 measurement prediction are configured to (relatively) reduce the UE burden for L1 measurement and reporting. For example, in some implementations, more resources can be reserved in candidate LTM cells.

[0184] Furthermore, regarding additional UE efforts in L1 mobility scenarios, for each candidate cell's TA acquisition, in the case of using a network-based / early TA acquisition mechanism, the UE may transmit preambles to one or more target cells, as discussed. Additionally, in the case of a UE-based / RSTD-based TA acquisition mechanism, it may be necessary to perform the measurement and calculation of the target cell's TA and report it to the network.

[0185] Therefore, this paper discusses various aspects of L1 measurement prediction and / or TA prediction. The first aspect is the overall process between the base station and the UE for using L1 measurement and / or TA prediction. Another aspect involves the process for training the ML model to be used for L1 and / or TA prediction. Yet another aspect involves the mechanism for performing inference on L1 measurement prediction and / or RSTD-based mechanism TA prediction. For example, the use of each of the UE-side model and the dual-side model for each of L1 measurement prediction and / or RSTD-based mechanism TA prediction is discussed (and these predictions can be spatial and / or temporal predictions, as discussed in more detail elsewhere). Furthermore, the use of network-side models and the inference performance of early TA acquisition mechanism TA prediction are discussed. In such cases, joint temporal-spatial predictions can be generated. Additional aspects include possible performance monitoring.

[0186] UE-side procedures for L1 measurement and / or TA measurement prediction This paper accordingly discusses the details of the generation and use of AI / ML-based L1 and / or TA measurement predictions for mobility enhancement at the UE. FIG. 10 A flowchart 1002 illustrates a UE-side procedure for predicting L1 and / or TA measurements, such as between UE 1004 and network 1006, according to an embodiment of this document. It should be noted that in some embodiments, the UE-side procedure for predicting L1 and / or TA measurements may also be combined with the use of a UE server 1008.

[0187] Flowchart 1002 begins with UE 1004 generating a UE capability report 1010 and sending it to network 1006. In some implementations, the UE capability report 1010 may include one or more of the following: whether the UE supports L1 time measurement prediction; whether the UE supports L1 spatial measurement prediction; whether the UE supports RSTD-based TA prediction in the time domain; whether the UE supports RS-RSTD-based TA prediction in the spatial domain; the maximum number of historical samples / time slots that can be used at the UE; the maximum number of prediction samples / time slots that can be used at the UE; and / or the maximum number of parallel predictions that can be used at the UE.

[0188] Then, network 1006 provides training configuration 1012 to UE 1004. Training configuration 1012 may include one or more of the following: the type of ML model to be trained (e.g., a Long Short-Term Memory (LSTM) ML model type, a Recurrent Neural Network (RNN) ML model type, etc.), the layers to be trained and / or one or more specialized ML models to be used; the window length corresponding to the history of measurements and / or predictions in the time and / or spatial domains to be used with the ML model; and / or the maximum number of parallel predictions that should be provided by UE 1004.

[0189] Then, UE 1004 performs data collection 1014. In some embodiments, this process incorporates generating / training an ML model at UE 1004 using the collected data. In some embodiments, UE 1004 provides the collected data to UE server 1008, such that offline training 1016 (e.g., generation of the ML model) occurs instead at UE server 1008, and the UE server then provides the thus generated ML model back to UE 1004.

[0190] Then UE 1004 transmits notification message 1018 to network 1006. The content of notification message 1018 may notify network 1006 which ML models (and in at least some cases, the model IDs corresponding to these ML models are available at UE 1004).

[0191] The content of notification message 1018 can notify network 1006 of model applicability conditions, which network 1006 can use to determine which ML model to use at UE 1004. It should be noted that model applicability conditions may include, for example, usage scenario information (e.g., indoor / outdoor), antenna type information, channel type information, UE speed information (e.g., UE travels less than 5 km / h (kmph)), UE altitude information (e.g., corresponding to UE movement at an elevation), etc.

[0192] The content of notification message 1018 can notify network 1006 of the UE's preferred model ID.

[0193] It should be noted that notification message 1018 may be, for example, part of an SR, part of a UAI, in a MAC-CE, or in the sending and receiving of RRC messages (e.g., in...). RRCReconfigurationComplete Provided in the message or a newly provided RRC message.

[0194] Based on notification message 1018, network 1006 can determine which ML model to activate at UE 1004 and can provide UE 1004 with activation message 1020 commanding UE 1004 to activate the selected ML model. Activation message 1020 may be provided, for example, as part of DCI, MAC-CE, or RRC message reception.

[0195] Then, UE 1004 continues to perform inference / prediction 1022 for L1 measurements and / or RSTD-based TA mechanism measurements (as applicable), as discussed, based on the configuration of network 1006. Inference / prediction 1022 can be reported to the network in report 1024.

[0196] It is conceivable that, regarding the UE-side process, UE 1004 or network 1006 may perform performance monitoring 1026 of the ML model (e.g., by comparing predicted measurements with corresponding actual measurements). Based on the results of performance monitoring 1026, UE 1004 or network 1006 may initiate LCM signaling 1028 for model switching or model deactivation, which then leads to model switching / deactivation 1030 at the UE. In the case of deactivation, UE 1004 and network 1006 may then fall back to a non-AI / ML-based measurement reporting solution.

[0197] Bilateral procedures for L1 and / or TA measurement prediction FIG. 11 A flowchart 1100 illustrates a two-sided process for predicting L1 and / or TA measurements, such as between UE 1102 and network 1104, according to an embodiment of this document. It should be noted that in some embodiments, the UE-side process for measurement prediction may also be combined with the use of a UE server 1106.

[0198] UE capability report 1010, training configuration 1012, data collection 1014, and offline training 1016 can all be found in this article. FIG. 10 The discussion regarding the UE-side process describes how it occurs.

[0199] Once the ML model exists at UE 1102, a model transfer 1108 occurs. During the model transfer, UE 1102 transmits the ML model to network 1104. The model transfer signaling can be RRC-based or DRB-based.

[0200] Then, UE 1102 can generate actual L1 measurements and / or actual RSTD-based TA measurements, and transmit a measurement report 1110 with these actual measurements to network 1104.

[0201] Network 1104 can then apply these actual L1 measurements / actual RSTD-based TA measurements together with the previously received ML model to perform L1 measurement / or RSTD TA measurement prediction 1112 (as applicable). In some implementations, the detailed prediction method may depend on the specific implementation of network 1104.

[0202] Furthermore, performance monitoring 1114 may occur at network 1104 (e.g., by comparing the actual L1 measurement and / or the actual RSTD-based TA measurement received from UE 1102 with the corresponding predicted L1 measurement and / or the predicted RSTD-based TA measurement).

[0203] Network 1104 can also be configured to trigger ML model retraining performance based on its specific implementation (which may make the decision, for example, at least in part, based on the results of performance monitoring 1114). As part of triggering this retraining, network 1104 may provide the UE with a retraining configuration 1116 that commands retraining (and may provide one or more parameters for the UE to analyze / use as part of the ML model retraining process).

[0204] In response to the retraining configuration 1116, in some implementations, the UE continues to perform data collection 1014, offline training 1016, and model transfer 1108 again, as previously described.

[0205] Example mechanisms for making L1 measurement predictions In some implementations, the UE may be configured (e.g., via RRC signaling) for one of several alternatives to L1 measurement prediction performed by an ML model (which may be referred to as a “measurement prediction model”). It should be noted that L1 measurement prediction as discussed herein may correspond to beam-level measurement (and this may not be explicitly mentioned in the various subsequent implementations).

[0206] In a first-class implementation for L1 measurement prediction, the L1 measurement prediction may correspond to a time prediction. For example, the measurement prediction model can be used to predict future L1 measurements based on the current inputs to the measurement prediction model.

[0207] FIG. 12 A mechanism 1200 for time prediction of L1 measurements according to the implementation discussed herein is illustrated. One or more actual L1 measurements 1202 (e.g., SSB and / or CSI-RS measurements) may be performed at the UE. The measurement prediction model may be configured to receive these one or more actual L1 measurements 1202 as input and, in response, provide one or more predicted L1 measurements 1204 (each of the one or more predicted L1 measurements 1204 corresponding to a later time). Each predicted L1 measurement in the predicted L1 measurements 1204 may be used, for example, for SSB or CSI-RS.

[0208] In a second type of implementation for L1 measurement prediction, L1 measurement prediction corresponds to spatial prediction. For example, the UE can use its understanding of applicable spatial channel statistics to predict / infer the (predicted) L1 measurement of a beam based on the actual or predicted L1 measurements of its neighboring beams. In such an example, the UE can predict the L1 measurement of a beam based on the actual L1 measurements of its neighboring beams.

[0209] FIG. 13A mechanism 1300 for spatial prediction of L1 measurements according to the implementation scheme discussed herein is illustrated. FIG. 13 An example of a spatial beaming arrangement 1302 for each of the first beam (“beam 1”), the second beam (“beam 2”), and the third beam (“beam 3”) is shown.

[0210] One or more actual L1 measurements can be performed. FIG. 13 In the example, the UE performs one or more first actual L1 measurements 1304 for the first beam (beam 1) and one or more second actual L1 measurements 1306 for the third beam (beam 3).

[0211] One or more actual L1 measurements (e.g., one or more first actual L1 measurements 1304 and one or more second actual L1 measurements 1306) are provided to a measurement prediction model 1308, which uses these terms to generate one or more predicted L1 measurements 1310 for a second beam (beam 2, as shown, which is a neighboring beam of beams 1 and 3). It should be noted that in some embodiments, the measurement prediction model 1308 may use applicable spatial channel statistics to generate one or more predicted L1 measurements 1310.

[0212] In some implementations, the predicted dwell / validity time can be provided for both the temporal and / or spatial predictions of L1 measurements. Furthermore, in some implementations, the confidence level of the prediction can be provided for both the temporal and / or spatial predictions of L1 measurements.

[0213] In some implementations, the temporal and spatial predictions of L1 measurements may be configured to be performed simultaneously in a two-dimensional space.

[0214] It should be noted that configuration information (e.g., RRC configuration information) can be provided to instruct the UE to perform L1 measurement predictions regarding the use of the measurement prediction model. For example, the UE can be configured to generate L1 measurement predictions for a specific beam of a particular cell (e.g., the current serving cell and / or neighboring cells).

[0215] As another example of using L1 measurement prediction based on configuration information, the UE can be configured to generate L1 measurement prediction based on one or more conditions.

[0216] In the first example using conditions for predicting L1 measurements, the UE can be configured to generate measurements for at most a number of... N L1 measurements for each beam, where the UE generates measurements for the number of previously known beams with the strongest RSRP / RSRQ. M beams ( M < N The actual L1 measurement of ) and further generate for the remainingN – M The predicted L1 measurement for each beam. The predicted L1 measurement falls within... M In the case of the highest measurement, the UE will then perform actual L1 measurements on subsequent beams (and the previous ones). M The last beam in the ensemble is added alternatively using the predicted beam. N – M gather).

[0217] In the second example using conditions for using predicted L1 measurements, a configured RSRP / RSRQ / SINR threshold can be compared with the actual or predicted L1 measurement. If the actual or predicted L1 measurement of the cell is less than the threshold, the UE performs L1 measurement prediction for the beam. In a variation of this, the base station may configure the UE to use separate thresholds for the use of actual and predicted L1 measurements.

[0218] In the third example using conditions for predicting L1 measurements, a configured interference measurement threshold can be used. In this case, the UE can use L1 measurement prediction if the interference intensity in the beam is higher than the threshold.

[0219] In the fourth example using conditions for using predicted L1 measurements, two thresholds can be used. A first configured RSRP threshold can be compared to the actual / predicted L3 cell-level measurement of the cell. If the actual or predicted L3 cell-level measurement of the cell is greater than the threshold, the UE can perform L1 (beam-level) measurement prediction for one or more beams in that cell. Then, a second configured RSRP / RSRQ threshold can be compared to the actual or predicted L1 measurement of the beams in that cell to determine whether beams in subsequent cells use actual L1 measurements or predicted L1 beam-level measurements.

[0220] In the fifth example using the conditions for using predicted L1 measurements, L1 measurement prediction can be performed for measurements across all frequencies.

[0221] In the sixth example using L1 measurements for prediction, L1 measurement prediction can be performed if the measurement uses a measurement gap (e.g., for a measurement of another frequency or another non-overlapping BWP).

[0222] As another example of using L1 measurement prediction based on configuration information, the configuration information may indicate neighboring cells and / or frequencies that allow the UE to autonomously / independently select to generate actual L1 measurements or predicted L1 measurements.

[0223] Implementations for reporting actual and / or predicted L1 measurements Imagine supporting periodic and event-triggered L1 measurement reports.

[0224] In some implementations, for periodic L1 measurement reporting, the UE can be configured by the network to have two periodicities in a single reporting configuration. The first periodicity can be a predicted measurement periodicity, indicating how often the UE should perform and / or report an AI / ML-based L1 measurement prediction. The second periodicity can be an actual measurement periodicity, indicating how often the UE should perform and / or report an actual L1 measurement. In some such implementations, the predicted measurement periodicity may be less than the actual measurement periodicity, allowing the UE to use L1 measurement predictions most of the time (e.g., to reduce power) while still occasionally reporting actual L1 measurements to provide more accurate updates / information available for model monitoring.

[0225] In some implementations, for event-triggered reporting, the UE may be configured to trigger a measurement reporting event using actual and / or predicted L1 measurements. Examples of such events may include a first event in which the actual or predicted L1 measurement is less than a threshold, a second event in which a first actual or predicted L1 measurement at the serving cell is less than a first threshold and a second actual or predicted L1 measurement at a neighboring cell is greater than a second threshold, and / or a third event in which the first actual or predicted L1 measurement at the neighboring cell is better than / greater than the second actual or predicted L1 measurement at the serving cell by more than / greater than a threshold.

[0226] The UE may be configured to determine whether a predicted L1 measurement can trigger a measurement reporting event (e.g., the opposite of triggering a measurement reporting event using only the actual L1 measurement). In such cases where a predicted L1 measurement is available, the UE may also be configured to trigger a measurement report based on the predicted L1 measurement (e.g., only if the confidence level for the predicted L1 measurement is greater than a threshold).

[0227] It should be noted that when analyzing L1 measurements of neighboring cells, the UE can be configured to determine whether the predicted L1 measurements are only for neighboring cells (and not, for example, the serving cell).

[0228] In these cases, the UE could also be configured to use, for example, one or more of events A1-A6, as can be understood in the NR wireless communication system regarding any predicted L3 cell-level measurement (e.g., if the confidence level for the predicted L3 cell-level measurement is greater than the corresponding threshold).

[0229] In either case, once the measurement reporting event is triggered, the UE can report the available predicted and / or actual L1 measurements as part of the measurement report.

[0230] In some implementations, for both periodic and event-triggered reporting, the UE may first report the actual L1 measurement and then report the predicted L1 measurement. In the first case, the order in which such L1 measurements are reported may then be based on the number of measurements (RSRP / RSRQ) (e.g., from high to low).

[0231] In the second case, the order in which such L1 measurements are reported can be based on the confidence level for any predictive measurement (e.g., from high to low).

[0232] In the third case, the order of reporting such L1 measurements could be to report the SSB measurement first, followed by the CSI-RS measurement.

[0233] It should be noted that any combination of the above three scenarios is possible. For example, the UE can be configured to report the SSB first, then the highest RSRP first, and finally the CSI-RS (e.g., in the case where more than one SSB has the same RSRP).

[0234] Within the measurement report, the UE indicates which L1 measurements are predictive measurements and / or which L1 measurements are actual measurements. In at least some embodiments, the UE may also indicate the reliability probability or confidence level of any predictive measurement if it includes predictive measurements in the measurement report.

[0235] Regarding the use of L1 measurement predictions, the total number of parallel predictions in the measurement report may be within the UE's capabilities. In some cases, the base station configures the UE to perform multiple predictions within that UE's capabilities (e.g., it may be selected to perform measurements corresponding to previously reported high channel conditions).

[0236] When a UE discards one or more measurement predictions to remain within its capabilities, the UE may discard predictions corresponding to poor channel conditions; discard predictions corresponding to poor confidence levels; and / or discard predictions based on CSI-RS. It should also be noted that any combination of these criteria can be used for discarding purposes.

[0237] Example mechanisms for making RSTD-based TA measurement predictions FIG. 14 Figure 1400 illustrates example scenarios of various TAs (Transmission Actions) between UE 1402 and each of the first cell 1404, the second cell 1406, and the third cell 1408. For example... FIG. 14As shown, each of the first cell 1404, the second cell 1406, and the third cell 1408 may be located at a different position relative to the UE 1402. Therefore, the first TA 1410 for communication between the UE 1402 and the first cell 1404, the second TA 1412 for communication between the UE 1402 and the second cell 1406, and the third TA 1414 for communication between the UE 1402 and the third cell 1408 may all be independent and / or different from each other.

[0238] In some implementations, the UE can be configured (e.g., via RRC signaling) for one of a variety of alternatives to RSTD-based TA measurement predictions performed by an ML model (which may be referred to as a "measurement prediction model").

[0239] In a first-class implementation for RSTD-based TA measurement prediction, the RSTD-based TA measurement prediction may correspond to time prediction. For example, the measurement prediction model can be used to predict future RSTD-based TA measurements based on the current inputs to the measurement prediction model.

[0240] FIG. 15 A mechanism 1500 for time prediction of RSTD-based TA measurements according to the implementation scheme discussed herein is illustrated. One or more actual RSTD-based TA measurements 1502 can be performed at the UE. The measurement prediction model can be configured to receive these one or more actual RSTD-based TA measurements 1502 as input and, in response, provide one or more predicted RSTD-based TA measurements 1504 (where each predicted RSTD-based TA in the one or more predicted RSTD-based TA measurements 1504 corresponds to a later time).

[0241] In a second type of implementation for RSTD-based TA measurement prediction, RSTD-based TA measurement prediction corresponds to spatial prediction. For example, a UE can use its understanding of applicable spatial channel statistics to predict / infer the (predicted) RSTD-based TA measurement of a cell based on the actual or predicted RSTD-based TA measurements of its neighboring cells. For example, a UE can predict the RSTD-based TA measurement of a cell based on the actual RSTD-based TA measurements of its neighboring cells.

[0242] For spatial prediction, the UE can predict the TA of a candidate cell based on the actual RSTD-based TA measurement of another candidate cell. In such cases, the network can provide the UE with various information (e.g., base station deployment geometry) as auxiliary information.

[0243] FIG. 16A mechanism 1600 for spatial prediction of RSTD-based TA measurements is illustrated according to the implementation scheme discussed herein. FIG. 16 Corresponding to the first cell (e.g., FIG. 14 The first community 1404), the second community (for example, FIG. 14 The second community 1406) and the third community (e.g., FIG. 14 The spatial arrangement of each of the three communities (1408).

[0244] One or more actual RSTD-based TA measurements can be performed. FIG. 16 In the example, the UE performs one or more first actual RSTD-based TA measurements 1602 in the first cell and one or more second actual RSTD-based TA measurements 1604 in the third cell.

[0245] One or more actual RSTD-based TA beamlevel measurements (e.g., one or more first actual RSTD-based TA measurements 1602 (e.g., for the first cell 1404) and one or more second actual RSTD-based TA measurements 1604 (e.g., for the third cell 1408) are provided to a measurement prediction model 1606, which uses these terms to generate one or more predicted RSTD-based TA beamlevel measurements 1608 for the second cell (e.g., the second cell 1406). It should be noted that in some embodiments, the measurement prediction model 1606 may use applicable spatial channel statistics to generate one or more predicted RSTD-based TA beamlevel measurements 1608.

[0246] In some implementations, the predicted dwell / validity time can be provided for both temporal and / or spatial predictions of RSTD-based TA measurements. Furthermore, in some implementations, the confidence level of the prediction can be provided for both temporal and / or spatial predictions of RSTD-based TA measurements.

[0247] In some implementations, temporal and spatial predictions of TA measurements based on RSTD may be configured to be performed simultaneously in a two-dimensional space.

[0248] Implementations for reporting actual and / or predicted RSTD-based TA measurements Imagine supporting periodic and event-triggered RSTD-based TA measurement reports.

[0249] In some implementations for periodic RSTD-based TA measurement reporting, the UE can be configured by the network to have two periodicities in a single reporting configuration. The first periodicity can be a predictive measurement periodicity, indicating how often the UE should perform and / or report an AI / ML-based RSTD-based TA measurement prediction. The second periodicity can be an actual measurement periodicity, indicating how often the UE should perform and / or report an actual RSTD-based TA measurement.

[0250] For an event-triggered reporting implementation, the UE may be configured with a new TA-related event (e.g., "Event-4"). This event may occur when the change in the RSTD-based TA of a candidate cell is greater than a threshold compared to the last reported instance of that value. The network can be configured to allow the UE to determine whether actual RSTD-based TA measurements and / or predicted RSTD-based TA measurements can trigger the event. Additionally or alternatively, the UE may be configured to trigger the event only when the confidence level of the predicted RSTD-based measurement is greater than a threshold.

[0251] In either case, once the measurement report event is triggered, the UE can report the available predicted and / or actual RSTD-based TA measurements as part of the measurement report.

[0252] In some implementations, for both periodic and event-triggered reporting, the UE may first report actual RSTD-based TA measurements, and then report predicted RSTD-based TA measurements. In the first case, the order in which these RSTD-based TA measurements are reported may then be based on the number of cell-level L3 measurements (RSRP / RSRQ / SINR) from high to low.

[0253] In the second case, the order in which such RSTD-based TA measurements are reported can be based on the confidence level for any predictive measurement (e.g., from high to low).

[0254] It should be noted that any combination of the two scenarios described above is possible. For example, the UE could be configured to first report based on the number of cell-level L3 measurements, and then secondarily based on the confidence level of any predictive RSTD-based TA measurements.

[0255] Within the measurement report, the UE may indicate which RSTD-based TA measurements are predictive measurements and / or which RSTD-based TA measurements are actual measurements. In at least some embodiments, the UE may also indicate the reliability probability or confidence level of any predictive measurement when it includes predictive measurements in the measurement report.

[0256] Regarding the use of RSTD-based TA measurement predictions, the total number of parallel predictions in the measurement report may be within the UE's capabilities. In some cases, any additional RSTD-based TA measurements / predictions may be discarded based on, for example, the order of measurement reports used for RSTD-based TA measurement predictions (e.g., as discussed herein).

[0257] Model monitoring and LCM for L1 measurement prediction and / or RSTD-based TA measurement prediction Imagine a UE-side procedure for L1 measurement prediction and / or RSTD-based TA measurement prediction, in which model monitoring (e.g., monitoring the performance of the ML model) can be performed at the UE and / or at the base station. In the case of performing model monitoring at the base station, the procedure may depend on the specific implementation at the base station.

[0258] In cases where model monitoring is performed at the UE, in some such implementations, the model monitoring metric used by the UE may be the error between the predicted L1 measurement / RSTD-based TA measurement and the corresponding actual L1 measurement / RSTD-based TA measurement. For example, the MSE between some predicted L1 measurement / RSTD-based TA measurement and the corresponding actual L1 measurement / RSTD-based TA measurement may be used (and it should be noted that using the MSE metric in this way could be an example of a "confidence level" as discussed herein). For monitoring purposes, the UE may perform both the predicted L1 measurement / RSTD-based TA measurement and the corresponding actual L1 measurement / RSTD-based TA measurement for a small set of cells and / or beams (as applicable).

[0259] It is conceivable that the ML model in use may be switched from time to time (e.g., a different ML model may be selected for use and / or the use of an ML model may be paused or stopped for prediction). This may occur, for example, based on the results of model monitoring, as will be described. The UE may be configured to perform UE-initiated model switching or network-initiated model switching.

[0260] In the case of a UE-initiated handover, the UE can be configured with conditions and UE behavior related to model metrics (e.g., confidence levels). For example, the UE can be configured with an MSE (Mean Sequence Size) threshold of 0.01. When the MSE is greater than 0.01, the UE can be configured to fall back to the normal measurement.

[0261] In the event of a network-initiated handover, the UE can report model monitoring metrics (e.g., confidence level) and may wait for base station LCM signaling in response (e.g., instructions to stop using the ML model and / or start using a different ML model). The UE can report model monitoring metrics via UAI or MAC-CE.

[0262] It should be noted that the network may provide the UE with auxiliary information about model monitoring occurring on the UE side, which the UE can use. This auxiliary information may include any one or more of the following: the geometry of nearby base station deployments; statistics on temporal correlations; and / or long-term statistics on inter-cell or inter-beam correlations.

[0263] For the two-sided process used for L1 measurement prediction and / or RSTD-based TA measurement prediction, model monitoring can be performed on the base station side, and it may depend on the specific implementation of the gNB.

[0264] Regarding the UE-side and / or dual-side processes for L1 measurement prediction and / or RSTD-based TA measurement prediction monitored by the base station using ML models, the UE may be configured to provide information to the base station to assist the base station in performing monitoring. This information may include additional temporal information, such as timestamps used for prediction and / or timestamps corresponding to actual measurements. This information may also / optionally include additional spatial information, such as the UE's actual location, the UE's actual movement orientation, changes in the UE's movement orientation, and / or incremental directions (differences) that can be compared with the prediction.

[0265] Base station-side procedures for early TA prediction FIG. 17A and FIG. 17B A flowchart 1700 illustrating the implementation of early TA prediction in a system comprising a UE 1702, a source base station 1704 communicating with the UE on the serving cell, a first target base station (“Target Base Station 1”) having a first target cell 1706, a second target base station (“Target Base Station 2”) having a second target cell 1708, and a server 1710, is illustrated below. It should be noted that a base station-side procedure may be appropriate regarding the use of the early TA mechanism, as TA is maintained at the base station side.

[0266] Flowchart 1700 begins with data collection and offline model training 1712. Then, UE 1702 transmits actual and / or predicted L3 cell-level and / or beam / level measurements 1714 to source base station 1704. These measurements can indicate to source base station 1704 that the first target cell 1706 and the second target cell 1708 are appropriate target cells.

[0267] As shown in the figure, the source base station 1704 continues to perform LTM candidate preparation 1716 with the first target cell 1706 and the second target cell 1708, so that the first target cell 1706 and the second target cell 1708 are ready to take action according to LTM.

[0268] Then, the source base station 1704 transmits a configuration message 1718 to the UE 1702, indicating candidate configurations for the first target cell 1706 and the second target cell 1708 (e.g., RRCReconfiguration (Message). Furthermore, the source base station 1704 may include an applicability condition request corresponding to the applicability conditions used to select the ML model to be used by the base station. For example, a configuration message 1718 may indicate that a UE speed threshold will be used to select a suitable ML model.

[0269] The UE may provide a configuration response 1720 to the source base station 1704 in response to receiving a configuration message 1718. The configuration response 1720 may include feedback on the applicability conditions indicated in the configuration message 1718 in the form of an applicability condition response, which informs the source base station 1704 of the value of the applicability condition. Based on the value of the applicability condition received in the configuration response 1720, the source base station 1704 may (e.g., from multiple such models that may exist at the source base station 1704) identify the ML model used for early TA prediction. It should be noted that it is envisioned that the UE may provide updated feedback (e.g., updated values ​​of the applicability conditions) to the source base station 1704 at any time via the UAI.

[0270] UE 1702 then transmits preamble 1722 to each of the first target cell 1706 and the second target cell 1708. As shown, UE 1702 transmits the first preamble to the first target cell 1706 at a first time ("T1"), the second preamble to the second target cell 1708 at a second time ("T2"), the third preamble to the first target cell 1706 at a third time ("T3"), and the fourth preamble to the second target cell 1708 at a fourth time ("T4").

[0271] After receiving the preamble 1722, the first target base station 1706 of the first target cell and the second target base station 1708 of the second target cell each transmit TA information 1724 to the source base station 1704. The TA information 1724 may include the collected data corresponding to the preamble 1722 and can be transmitted to the source base station 1704 through an inter-node signaling process.

[0272] TA information 1724 may include the UE ID of UE 1702.

[0273] The TA information 1724 used for transmitting to the target cell may also include a TA value corresponding to the transmission by UE 1702 to the target cell. As discussed herein, this TA value may have already been determined at the network using the timing corresponding to one or more preambles transmitted by UE 1702 to the target cell.

[0274] The TA information 1724 for transmitting the target cell may also include one or more timestamps corresponding to the preamble used to generate the reported TA value for transmitting the target cell (e.g., such as...). FIG. 17B (As shown in TA information 1724).

[0275] It should be noted that TA information 1724 can generally be considered to include additional instances of similar communications between the first target cell 1706 and the second target cell 1708, in addition to those explicitly illustrated (e.g., it may include unillustrated information about a previous unillustrated preamble that predates 1722).

[0276] The source base station 1704 may then receive one or more actual or predicted L1 measurements 1726 from the UE 1702. Based on these actual or predicted L1 measurements 1726, the source base station 1704 may make a HO decision 1728, in which it selects one of the first target cell 1706 and the second target cell 1708 where the UE should perform the handover.

[0277] After making the HO decision 1728, the source base station 1704 can apply the TA information 1724 to the selected ML model to generate a predicted early TA 1730. In some examples, the predicted early TA 1730 is a joint temporal-spatial prediction.

[0278] The predicted early TA 1730 can then be included in a cell handover command 1732 (e.g., a MAC-CE cell handover command), which is transmitted to UE 1702 to cause the UE to perform LTM for the selected target cell (one of the first target cell 1706 and the second target cell 1708). As part of this LTM, the UE uses the predicted early TA 1730 received from the source base station 1704 to adjust its transmission timing with respect to the selected target cell.

[0279] It should be noted that in such implementations, model monitoring can be performed on the network side and may depend on the specific network implementation.

[0280] Implementations for failure handling using predicted L1 measurements In some implementations, if LTM execution has failed (e.g., caused / determined by the expiration of an LTM supervisor timer), the UE may consider (potentially) performing the cell selection process using predicted L1 measurements. For example, if the configured candidate target cells become suitable, the UE may select a target cell via the following priority ranking rules. First, the UE may select from cells with actual L1 measurements (e.g., L1 RSRP / RSRQ) from high to low. Then, the UE may select cells following a confidence level that only has predicted L1 measurements.

[0281] It should be noted that these priority ordering rules are given as examples rather than as restrictions. It is conceivable that the priority ordering rules used in this case may vary depending on the specific implementation of the UE.

[0282] Conditional handover method CHO (Crossover Event) is a feature used to improve mobility robustness. In a CHO, a UE can be configured with a handover command and associated CHO conditions to be monitored (sometimes alternatively referred to as "event conditions," "trigger conditions," or "conditions"). When the associated condition becomes true, the UE can execute the stored handover command. Event conditions may include, for example, when a neighboring cell becomes better than a specific cell (SpCell) by a certain offset (e.g., event condition A3), or when SpCell becomes worse than a first threshold and a neighboring cell becomes better than a second threshold (e.g., event condition A5). SpCell is the primary serving cell of a primary cell group (MCG) or secondary cell group (SCG), and the offset can be positive or negative. When more than one candidate target cell meets the condition, the cell for which the UE executes the HO may be determined depending on the specific UE implementation. In some wireless communication systems (e.g., 3GPP Release 17 wireless communication systems), new location- and time-related trigger conditions can be defined to help enhance CHO for non-terrestrial networks (NTNs).

[0283] FIG. 18A and FIG. 18B A flowchart 1800 illustrating conditional handover that can be used in some wireless communication systems is provided. Flowchart 1800 illustrates a wireless communication system including a UE 1802, a source gNB 1804, a target gNB 1806, other potential target gNBs 1808, an Access and Mobility Management Function (AMF) 1810, and one or more User Plane Functions (UFP) 1812. It can be seen that flowchart 1800 corresponds to the situation within the AMF / UPF. It should be noted that in other implementations, the source gNB 1804, the target gNB 1806, and other potential target gNBs 1808 may each (e.g., independently) be a base station type other than a gNB.

[0284] like FIG. 18AAs illustrated, flowchart 1800 begins with the handover preparation phase 1814. Currently, as shown, user data 1816 is transmitted between UE 1802 and source gNB 1804, and between source gNB 1804 and UFP 1812. AMF 1810 provides mobility control information 1818 to source gNB 1804. Then, during measurement control and reporting 1820, source gNB 1804 configures measurements at UE 1802, and UE 1802 performs the measurements and reports the measurement results to source gNB 1804. Based on the receipt of the measurement report, source gNB 1804 makes a CHO decision 1822. Based on CHO decision 1822, source gNB 1804 transmits handover request 1824 to other gNBs (in flowchart 1800, target gNB 1806, which will eventually be selected as the target of handover, and other potential target gNB 1808 are both represented as receiving handover request 1824).

[0285] Other gNBs (e.g., target gNB 1806 and other potential target gNBs 1808) each perform admission control 1826 and respond to source gNB 1804 with a handover request confirmation 1828, including the configuration of any CHO candidate cells at that gNB.

[0286] FIG. 18B Continuing from the previous article about FIG. 18A The flowchart 1800 under discussion. Source gNB 1804 transmits to UE 1802 the configuration for the CHO candidate cell (CHO configuration for the candidate cell). RRCReconfiguration Message 1830. UE1802 transmits to source gNB 1804 RRCReconfigurationComplete Message 1832.

[0287] Then, flowchart 1800 proceeds to the handover execution phase 1834. UE 1802 evaluates 1836 the CHO condition. Furthermore, in some implementations (e.g., where early data forwarding is used), target gNB 1806 transmits an early status transfer message 1838 to other potential target gNBs 1808.

[0288] Then, UE 1802 leaves the old cell 1840 and synchronizes to the new cell (e.g., on target gNB 1806). As part of this process, the UE performs an evaluation of the conditions on the candidate cells and determines that the new cell (on target gNB 1806) meets these conditions and is therefore to be handed over to that cell. The configuration for the new cell is then applied at the UE.

[0289] Furthermore, user data 1842 is transmitted between UFP 1812 and target gNB 1806 and / or other potential target gNB 1808 via source gNB 1804. Once UE 1802 becomes associated with a new cell on source gNB 1804 (and UE 1802 can transmit accompanying data to target gNB 1806), RRCReconfigurationComplete (News) The transfer of CHO was completed in 1844.

[0290] Then, flowchart 1800 proceeds to the handover completion phase 1846. First, the target gNB 1806 transmits a handover success message 1848 to the source gNB 1804. Next, the source gNB 1804 transmits a sequence number (SN) status transfer 1850 to the target gNB 1806. User data 1852 is transmitted between UFP 1812 and the target gNB 1806 via the source gNB 1804. Finally, the source gNB 1804 may transmit a handover cancellation message 1854 to the target gNB 1806 and / or other potential target gNBs 1808.

[0291] Overview of measurement prediction used in CHO methods The details of generating and using AI / ML-based CHO enhancements at the UE will now be discussed. In some cases, using AI / ML-based CHO enhancements may result in a reduction of reserved radio resources for candidate target cells based on predicted L3 measurements. In such cases, the UE may suggest changes to unfavorable CHO conditions, such as candidate target cells that are unlikely to meet CHO conditions within the relevant timeframe, CHO event types, and / or applicable thresholds.

[0292] In some cases, using AI / ML-based CHO enhancements can lead to a reduction in the time required to initiate CHO execution. For example, CHO can be executed when the predicted L3 measurement meets the CHO condition (e.g., instead of waiting for the actual L3 measurement to meet the CHO condition).

[0293] In some cases, using AI / ML-based CHO enhancements can make the UE's target cell selection more robust. Depending on the specific UE implementation, the target cell selection may vary depending on the existing / defined CHO procedures, provided that more than one target cell meets the applicable CHO conditions. Therefore, in some cases, measurement predictions can be used to further select among such cells.

[0294] This paper discusses various aspects of the overall process between the base station and the UE regarding the AI / ML-enhanced CHO mechanism. Furthermore, it discusses new CHO configurations that can be used in such environments. Further, it discusses UE behavior regarding CHO condition evaluation in such environments. Still further, it discusses what can be provided from the UE to the source cell / source base station (e.g., via...) in such environments. RRCReconfigurationComplete Messages or UAI (User AI) auxiliary information. Finally, various aspects of UE behavior used for fault handling in such environments are discussed.

[0295] Procedure for CHO using measurement prediction FIG. 19 A flowchart 1900 illustrates a CHO process for using a UE 1902, a source base station 1904 communicating with the UE on a serving cell, a first target base station (“Target Base Station 1”) having a first target cell 1906, a second target base station (“Target Base Station 2”) having a second target cell 1908, and a server 1910, according to the implementation scheme discussed herein.

[0296] As shown in the figure, UE 1902, source base station 1904, and server 1910 work together to generate one or more L3 cell-level and / or beam-level measurement predictions 1912 corresponding to one or more of the first target cell 1906 and / or the second target cell 1908. These are ultimately reported by UE 1902 to source base station 1904. This can occur as discussed elsewhere in this document.

[0297] Then, the source base station 1904 may perform a first CHO preparation 1914 with respect to the first target cell 1906, as shown in the figure. The first CHO preparation 1914 may be based on the prediction of the L3 cell / beam level measurement of the first target cell 1906 reported by the UE (and in some cases, this may include analysis of any corresponding confidence levels of these predicted L3 measurements, as may also be provided by the UE 1702).

[0298] As shown in the figure, the source base station 1904 can also perform a similar second CHO preparation 1916 with respect to the second target cell 1908 (and for similar reasons).

[0299] In some cases, the specific metrics and procedures for selecting cells for CHO preparation, as described, may vary depending on the specific implementation of the source cell.

[0300] The UE source base station 1904 then provides the CHO configuration 1918 to the UE 1902 (e.g., in...). RRCReconfiguration(As shown in the message). As illustrated, in some environments, CHO configuration 1918 can be understood as a CHO command. CHO configuration 1918 provides a list of candidate target cells, which identifies each of the first target cell 1906 and the second target cell 1908 as a candidate target cell.

[0301] Furthermore, CHO configuration 1918 may include one or more CHO events corresponding to one or more CHO conditions (e.g., relevant thresholds for measurement), which will be evaluated with respect to candidate target cells to determine whether a HO should be performed on a particular candidate target cell. As an example, for some 3GPP wireless communication systems, these CHO events may include events A3, A4, and / or A5.

[0302] For each measurement-based CHO event in a configuration, CHO configuration 1918 can provide an indication of whether the predicted L3 measurement can be used to evaluate the corresponding CHO condition.

[0303] In addition, for each configuration of measurement-based CHO events, CHO configuration 1918 can provide a confidence threshold (in... FIG. 19 The term "th" is used to evaluate CHO conditions using predicted L3 measurements where the use of predicted L3 measurements is permitted.

[0304] The CHO configuration 1918 is envisioned to include multiple sets of CHO conditions and criteria for selecting a specific CHO condition from these conditions for use. For example, the use of various such CHO conditions could be used to indicate a UE mobility speed threshold.

[0305] In some cases, CHO configuration 1918 may also include a priority value for each candidate target cell.

[0306] In response to receiving CHO configuration 1918, UE 1902 provides CHO configuration response 1920 to source base station 1904 (e.g., RRCReconfigurationComplete (Message). The UE can include various information in the CHO configuration response 1920.

[0307] In some cases, the CHO configuration response 1920 may include a proposed change to the list of target cells used by the source base station 1904. This can help reduce any mismatch between the L3 measurement reports predicted by the UE 1902 and the currently applicable channel observations (e.g., corresponding to CHO configuration 1918), which may be outdated.

[0308] In some cases, CHO configuration response 1920 may include updated L3 cell-level / beam-level measurement predictions. This helps reduce any mismatches between the L3 measurement reports predicted by UE 1902 and currently applicable channel observations (e.g., corresponding to CHO configuration 1918), which may be outdated.

[0309] In some cases, the CHO configuration response 1920 may include a prediction error metric for one of the predicted L3 measurements (e.g., the MSE between the predicted L3 measurement and its actual L3 measurement). In some cases, the CHO configuration response 1920 may include suggested changes to CHO configuration information, including but not limited to changes to the CHO event type; changes to the CHO conditions / thresholds used to assess whether a CHO event has occurred; changes to the TTT value; and / or changes to the suggested priority value for the cell.

[0310] It should be noted that although CHO configuration response 1920 has been cited as using RRCReconfigurationComplete The message, but also envisions that the information found therein could be carried alternatively / additionally in MAC-CE and / or a new / some other UL RRC message.

[0311] It should be noted that in at least some cases, the content of CHO configuration response 1920 may depend on the content of CHO configuration 1918.

[0312] As shown in the figure, source base station 1904 may optionally provide UE 1902 with an update 1922 on the CHO configuration (e.g., based on information received in the CHO configuration response 1920). The mechanism for such an update may vary depending on the specific implementation of source base station 1904. As shown in the figure, update 1922 may be available in... RRCReconfiguration The message is transmitted, and the UE may further provide the source base station 1904 with a response 1924 to the update 1922 (e.g., RRCReconfigurationComplete information).

[0313] Then, UE 1902 continues to CHO evaluation cycle 1926, in which the CHO conditions configured by CHO are actively monitored regarding the actual L3 cell-level and / or beam-level measurements and / or predicted L3 cell-level and / or beam-level measurements generated from time to time at UE 1902.

[0314] It should be noted that during the CHO evaluation cycle 1926, the UE may transmit a UAI message 1928 to the source base station 1904 to perform one or more of the following: a proposed change to the candidate target cell list; a proposed target cell for performing an unconditional (e.g., regular) HO; and / or a proposed change to the priority value of one or more cells.

[0315] As shown in the figure, the CHO evaluation cycle 1926 ends when UE 1902 determines that the CHO conditions for an applicable CHO event with applicable CHO configuration have been met. Regarding the implementation using predicted L3 measurements, it can be assumed that these CHO conditions are met under various possible circumstances.

[0316] In the first case, the CHO condition is considered met when the actual L3 measurement satisfies the CHO condition, or when the predicted L3 measurement satisfies the CHO condition and has a corresponding confidence level greater than the threshold.

[0317] In the second case, the CHO condition is considered met when both the actual L3 measurement and the predicted L3 measurement satisfy the CHO condition, and when the predicted L3 measurement has a corresponding confidence level greater than the threshold.

[0318] In the third case, the CHO condition is considered met when the predicted L3 measurement satisfies the CHO condition and has a corresponding confidence level greater than the threshold (e.g., without referencing the actual L3 measurement in one way or another).

[0319] It should be noted that the additional case corresponds to the CHO condition to be satisfied when the actual L3 measurement satisfies the CHO condition (e.g., not referencing the predicted L3 measurement in one way or another).

[0320] The UE behavior regarding which of these conditions must be met for the CHO condition to be considered can be configured by the source base station 1904 at the UE 1902 (e.g., via RRC signaling).

[0321] If more than one target cell (e.g., each of the first target cell 1906 and the second target cell 1908) satisfies the CHO condition, the UE may select one of the target cells based on the configured priority values ​​of these target cells. Regarding this mechanism, it should be noted that the source base station 1904 may use DL signaling (e.g., MAC-CE or DCI) to cause a change in the priority of the target cell.

[0322] Flowchart 1900 illustrates the scenario where UE 1902 determines that the second target cell 1908 meets the CHO conditions for the applicable CHO event of CHO configuration 1918 and therefore performs a HO to it. As shown, UE 1902 performs RACH procedure 1930 with the second target cell 1908 to attach to the second target cell 1908. Once the HO to the second target cell 1908 is completed, UE 1902 sends a CHO completion message 1932 to the second target cell 1908 (e.g., ...). RRCReconfigurationComplete The CHO completion message (via the second target cell 1908) indicates to the network that the CHO has occurred.

[0323] Implementation of LTM failure handling for CHO procedure using predicted measurements If CHO execution fails (e.g., due to a RACH failure with the selected target cell), the UE may perform the cell selection process by considering the information provided in CHO Configuration 1918. For example, if the configured cell is suitable, the UE may select the target cell via the following priority ranking rules. First, the UE may follow any configured priority value (if configured). Then, the UE may select the cell based on whether the relevant CHO conditions satisfy the actual measurements of the cell. Finally, the UE may select the cell based on the confidence level (for predicted measurements).

[0324] It should be noted that these priority ordering rules are given as examples rather than as restrictions. It is conceivable that the priority ordering rules used in this case may vary depending on the specific implementation of the UE.

[0325] FIG. 20 A method 2000 for a UE according to the embodiments discussed herein is illustrated. Method 2000 includes transmitting to the network 2002 a notification message identifying one or more measurement prediction models at the UE. Method 2000 also includes receiving from the network 2004 an activation message for a first measurement prediction model available for use among the one or more measurement prediction models at the UE. Method 2000 further includes generating 2006 one or more actual measurements of one or more reference signals received at the UE from a cell of the network. Method 2000 further includes generating 2008 one or more predicted measurements based on the one or more actual measurements using the first measurement prediction model. Method 2000 further includes transmitting to the network 2010 a first measurement report including one or more predicted measurements.

[0326] In some implementations of method 2000, one or more predicted measurements include one or more predicted L3 cell-level measurements.

[0327] In some such implementations, one or more actual measurements include one or more actual L3 cell-level measurements for one or more reference signals received at the UE; and one or more predicted L3 cell-level measurements are generated by providing one or more actual L3 cell-level measurements to the first measurement prediction model using a first measurement prediction model.

[0328] In some such implementations, one or more actual measurements include one or more actual L1 beam-level measurements of one or more reference signals received at the UE; and one or more predicted L3 cell-level measurements are generated using a first measurement prediction model by: providing one or more actual L1 beam-level measurements to the first measurement prediction model to generate one or more predicted L1 beam-level measurements; and performing linear averaging and L3 filtering on the one or more actual L1 beam-level measurements and the one or more predicted L1 beam-level measurements. In some of these cases, the L3 filter coefficients used for L3 filtering are generated by the measurement prediction model.

[0329] In some such implementations, one or more actual measurements include one or more actual L1 beam-level measurements of one or more reference signals received at the UE; and one or more predicted L3 cell-level measurements are generated using a first measurement prediction model by: performing a linear average on one or more actual L1 beam-level measurements; and providing the first measurement prediction model with one or more actual linear average results and a configured set of L3 filter coefficients.

[0330] In some such implementations, one or more actual measurements include one or more actual L1 beam-level measurements of one or more reference signals received at the UE; and one or more predicted L3 cell-level measurements are generated using a first measurement prediction model by providing one or more actual L1 beam-level measurements and a set of L3 filter coefficients configured to the first measurement prediction model.

[0331] In some such implementations, one or more predicted L3 cell-level measurements are for the cell’s neighboring cells; and the generation of one or more predicted L3 cell-level measurements using a first measurement prediction model is also based on the correlation information of the neighboring cells.

[0332] In some such implementations, method 2000 further includes receiving configuration information identifying frequencies from the network, and one or more of the predicted L3 cell-level measurements are frequency-specific.

[0333] In some such implementations, method 2000 further includes receiving configuration information from the network that identifies conditions for generating one or more predicted L3 cell-level measurements, wherein the one or more predicted L3 cell-level measurements are generated in response to a determination at the UE that the conditions have been met.

[0334] In some such implementations, method 2000 further includes receiving configuration information identifying a cell from the network, and wherein the UE selects to generate one or more predicted L3 cell-level measurements based on the cell identification in the configuration information.

[0335] In some embodiments of method 2000, one or more predicted measurements include one or more predicted L3 beam-level measurements.

[0336] In some such implementations, one or more actual measurements include one or more actual L3 beam-level measurements of one or more reference signals received at the UE; and one or more predicted L3 beam-level measurements are generated by providing one or more actual L3 beam-level measurements to the first measurement prediction model using a first measurement prediction model.

[0337] In some such implementations, one or more actual measurements include one or more actual L1 beam-level measurements of one or more reference signals received at the UE; and one or more predicted L3 beam-level measurements are generated using a first measurement prediction model by: providing one or more actual L1 beam-level measurements to the first measurement prediction model to generate one or more predicted L1 beam-level measurements; and performing L3 filtering on the one or more actual L1 beam-level measurements and the one or more predicted L1 beam-level measurements.

[0338] In some such implementations, one or more actual measurements include one or more actual L1 beam-level measurements of one or more reference signals received at the UE; and one or more predicted L3 beam-level measurements are generated using a first measurement prediction model by providing one or more actual L1 beam-level measurements to the first measurement prediction model.

[0339] In some such implementations, one or more reference signals are received on one or more beams; one or more predicted L3 beam-level measurements are for neighboring beams of one or more beams that are not part of one or more beams; and the generation of one or more predicted L3 beam-level measurements using a first measurement prediction model is also based on statistical information about the channel between the UE and the cell.

[0340] In some such implementations, method 2000 further includes receiving configuration information identifying the beam from the network, and one or more of the predicted L3 beam-level measurements are beam-specific.

[0341] In some such implementations, method 2000 further includes receiving configuration information from the network that identifies conditions for generating one or more predicted L3 beam-level measurements, wherein the one or more predicted L3 beam-level measurements are generated in response to a determination at the UE that the conditions have been met.

[0342] In some such implementations, method 2000 further includes receiving configuration information identifying a beam from the network, and wherein the UE selects to include predicted L3 cell-level measurements for the beam in one or more predicted L3 beam-level measurements based on the beam identifier in the configuration information.

[0343] In some embodiments of method 2000, one or more predicted measurements include one or more predicted L1 beam-level measurements.

[0344] In some such implementations, one or more actual measurements include one or more actual L1 beam-level measurements for one or more reference signals received at the UE; and one or more predicted L1 beam-level measurements are generated by providing one or more actual L1 beam-level measurements to the first measurement prediction model using a first measurement prediction model.

[0345] In some such implementations, one or more reference signals are received on one or more beams; one or more predicted L1 beam-level measurements are made for neighboring beams of one or more beams that are not part of one or more beams.

[0346] In some such implementations, method 2000 further includes receiving configuration information identifying the beam from the network, and one or more of the predicted L1 beam-level measurements are beam-specific.

[0347] In some such implementations, method 2000 further includes receiving configuration information from the network that identifies conditions for generating one or more predicted L1 beam-level measurements, wherein the one or more predicted L1 beam-level measurements are generated in response to determining at the UE that the conditions have been met.

[0348] In some such implementations, method 2000 further includes receiving configuration information identifying a beam from the network, and wherein the UE selects to include predicted L1 cell-level measurements for the beam in one or more predicted L1 beam-level measurements based on the beam identifier in the configuration information.

[0349] In some implementations, method 2000 further includes receiving configuration information from the network indicating predicted measurement periodicity and actual measurement periodicity; and transmitting a second measurement report to the UE based on the actual measurement periodicity, including one or more actual measurements; wherein a first measurement report including one or more predicted measurements is transmitted based on the predicted measurement periodicity.

[0350] In some embodiments of method 2000, the transmission of a first measurement report to the network, which includes one or more predicted measurements, is triggered by one or more predicted measurements.

[0351] In some embodiments of method 2000, the transmission of a first measurement report to the network, which includes one or more predicted measurements, is triggered by actual measurements of one or more reference signals.

[0352] In some embodiments of method 2000, the first measurement report also includes an indication that one or more predicted measurements are predictive measurements.

[0353] In some embodiments of method 2000, the first measurement report also includes one or more actual measurements of one or more reference signals.

[0354] In some embodiments of method 2000, the notification message further includes one or more of the following: the predicted optimal L3 filter coefficients; the predicted optimal measurement report event type; the predicted optimal TTT for the measurement report event; the predicted optimal threshold for the measurement report event; the recommended cell for the actual measurement; the recommended beam for the first actual measurement in one or more actual measurements; and the recommended T304 timer value.

[0355] In some implementations, method 2000 further includes transmitting auxiliary information to the network, which includes one or more of the following: the deployment geometry of nearby base stations; a first statistic corresponding to temporal correlation; a second statistic corresponding to inter-cell correlation; and a third statistic corresponding to inter-beam correlation.

[0356] FIG. 21 Method 2100 of a RAN according to the implementation discussed herein is illustrated. Method 2100 includes receiving 2102 a measurement prediction model from a UE. Method 2100 also includes transmitting 2104 one or more reference signals to the UE. Method 2100 also includes receiving 2106 actual measurements of the one or more reference signals from the UE. Method 2100 also includes generating 2108 one or more predicted measurements based on the actual measurements of the one or more reference signals using the measurement prediction model; wherein the measurement prediction model is one of the following: an L3 cell-level measurement prediction model; an L3 beam-level measurement prediction model; and an L1 beam-level measurement prediction model.

[0357] In some implementations, method 2100 also includes transmitting configuration information to the UE for use by the UE to retrain the measurement prediction model.

[0358] In some implementations, method 2100 also includes receiving a timestamp from the UE to generate the actual measurement.

[0359] In some implementations, method 2100 further includes receiving the UE's location and the UE's movement orientation from the UE.

[0360] In some implementations, method 2100 also includes receiving a change in the UE's mobility orientation from the UE.

[0361] FIG. 22 A method 2200 for a UE according to the implementation discussed herein is illustrated. Method 2200 includes generating 2202 one or more predictions based on a first reference signal received at the UE from a cell in the network using a measurement prediction model. Method 2200 also includes generating 2204 one or more actual measurements corresponding to the one or more predictions by measuring a second reference signal received at the UE from the cell. 2200 further includes calculating 2206 a confidence level using the one or more predicted measurements and the one or more actual measurements. Method 2200 also includes reporting 2208 the confidence level to the network.

[0362] In some implementations of method 2200, calculating the confidence level includes determining the MSE between the predicted measurement and the actual measurement.

[0363] In some implementations, method 2200 also includes receiving an instruction from the network to stop using the measurement prediction model.

[0364] In some implementations, method 2200 further includes reporting to the network a first timestamp of the generated predicted measurement and a second timestamp of the generated actual measurement.

[0365] In some implementations, method 2200 also includes reporting the UE's location and UE's movement orientation to the network.

[0366] FIG. 23 Method 2300 of a UE according to the implementation discussed herein is illustrated. Method 2300 includes generating 2302 one or more predictions based on a first reference signal received at the UE from a cell in the network using a measurement prediction model. Method 2300 also includes generating 2304 one or more actual measurements corresponding to the one or more predictions by measuring a second reference signal received at the UE from the cell. Method 2300 further includes calculating 2306 a confidence level using the one or more predicted measurements and the one or more actual measurements. Method 2300 also includes stopping 2308 the use of the measurement prediction model based on the confidence level.

[0367] In some implementations of method 2300, calculating the confidence level includes determining the MSE between the predicted measurement and the actual measurement.

[0368] FIG. 24Method 2400 of a UE according to the implementation scheme discussed herein is illustrated. Method 2400 includes transmitting to the network 2402 a notification message identifying one or more RSTD-based TA prediction models at the UE. Method 2400 also includes receiving from the network 2404 an activation message identifying a first RSTD-based TA prediction model available for use among the one or more RSTD-based TA prediction models at the UE. 2400 further includes generating 2406 one or more actual RSTD-based TA measurements based on one or more reference signals received at the UE from one or more target cells of the network and the TA value of the serving cell of the network. Method 2400 further includes generating 2408 one or more predicted RSTD-based TA measurements of the first target cell based on one or more actual RSTD-based TA measurements of the one or more target cells using the first RSTD-based TA prediction model. Method 2400 also includes transmitting to the network 2410 an RSTD-based TA prediction report including one or more predicted RSTD-based TA measurements.

[0369] In some embodiments of method 2400, one or more reference signals are reference signals of the first target cell.

[0370] In some embodiments of method 2400, one or more reference signals are reference signals not transmitted by the first target cell.

[0371] In some implementations of method 2400, the RSTD-based TA prediction report also includes the validity period of one or more predicted RSTD-based TA measurements for the first target cell.

[0372] In some implementations of method 2400, the RSTD-based TA prediction report also includes confidence levels of one or more predicted RSTD-based TA measurements for the first target cell.

[0373] In some implementations, method 2400 further includes receiving configuration information from the network indicating the predicted RSTD-based TA measurement periodicity and the actual RSTD-based TA measurement periodicity; and transmitting to the UE an actual RSTD-based TA report including one or more actual RSTD-based TA measurements of a first target cell according to the actual RSTD-based TA measurement periodicity; including that the RSTD-based TA prediction report of one or more predicted RSTD-based TA measurements of the first target cell is transmitted according to the predicted RSTD-based TA measurement periodicity.

[0374] In some embodiments of method 2400, transmitting an RSTD-based TA prediction report to the network, which includes one or more predicted RSTD-based TA measurements, is triggered by the UE determining that a first predicted RSTD-based TA measurement in one or more predicted RSTD-based TA measurements of the first target cell differs from a previously predicted RSTD-based TA measurement of the first target cell by at least a threshold.

[0375] In some embodiments of method 2400, the RSTD-based TA prediction report also includes an indication that one or more predicted RSTD-based TA measurements are predictive RSTD-based TA measurements.

[0376] In some implementations of method 2400, the RSTD-based TA prediction report also includes one or more actual measurements of one or more reference signals.

[0377] In some implementations of method 2400, one or more predicted RSTD-based TA measurements are first ranked in an RSTD-based TA prediction report based on the Layer 3 (L3) measurement of the corresponding cell and then based on the confidence level associated with one or more predicted RSTD-based TA measurements.

[0378] FIG. 25 Method 2500 of a RAN according to the implementation discussed herein is illustrated. Method 2500 includes receiving 2502 an RSTD-based TA prediction model from a UE. Method 2500 also includes transmitting 2504 one or more reference signals from one or more target cells to the UE. Method 2500 also includes receiving 2506 one or more actual RSTD-based TA measurements of the one or more reference signals from the UE. Method 2500 also includes generating 2508 one or more predicted RSTD-based TA measurements of a first target cell based on one or more actual RSTD-based TA measurements of the one or more target cells using the RSTD-based TA prediction model.

[0379] In some implementations, method 2500 also includes transmitting configuration information to the UE for use by the UE to retrain the measurement prediction model.

[0380] In some implementations, method 2500 further includes receiving from the UE a timestamp for generating one or more actual RSTD-based TA measurements.

[0381] In some implementations, method 2500 further includes receiving the UE's location and the UE's movement orientation from the UE.

[0382] In some implementations, method 2500 also includes receiving a change in the UE's mobility orientation from the UE.

[0383] FIG. 26 Method 2600 of a UE according to the implementation scheme discussed herein is illustrated. Method 2600 includes generating 2602 one or more predicted RSTD-based TA measurements based on a first reference signal received at the UE from one or more target cells of the network using an RSTDTA prediction model. Method 2600 also includes generating 2604 one or more actual RSTD-based TA measurements corresponding to the one or more predicted RSTD-based TA measurements by measuring a second reference signal received at the UE from one or more target cells. Method 2600 further includes calculating 2606 a confidence level using the one or more predicted RSTD-based TA measurements and the one or more actual RSTD-based TA measurements. Method 2600 also includes reporting 2608 the confidence level to the network.

[0384] In some implementations of method 2600, calculating the confidence level includes determining the MSE between the predicted RSTD-based TA measurement and the actual RSTD-based TA measurement.

[0385] In some implementations, method 2600 also includes receiving an instruction from the network to stop using the RSTD-based TA prediction model.

[0386] In some implementations, method 2600 further includes reporting to the network a first timestamp of generating the predicted RSTD-based TA measurement and a second timestamp of generating the actual RSTD-based TA measurement.

[0387] In some implementations, method 2600 also includes reporting the UE's location and UE's movement orientation to the network.

[0388] In some implementations, method 2600 also includes receiving a change in the UE's mobility orientation from the UE.

[0389] FIG. 27 Method 2700 of a UE according to the implementation discussed herein is illustrated. 2700 includes generating 2702 one or more predicted RSTD-based TA measurements based on a first reference signal received at the UE from one or more target cells of the network using an RSTD TA prediction model. Method 2700 also includes generating 2704 one or more actual RSTD-based TA measurements corresponding to the one or more predicted RSTD-based TA measurements by measuring a second reference signal received at the UE from one or more target cells. Method 2700 further includes calculating 2706 a confidence level using the one or more predicted RSTD-based TA measurements and the one or more actual RSTD-based TA measurements. 2700 also includes stopping the use of the RSTD-based TA prediction model based on the confidence level.

[0390] In some implementations of method 2700, calculating the confidence level includes determining the MSE between the predicted RSTD-based TA measurement and the actual RSTD-based TA measurement.

[0391] FIG. 28 A method 2800 for a source base station of a RAN according to an embodiment discussed herein is illustrated. Method 2800 includes receiving 2802 TA information corresponding to communication between a UE and one or more target cells from one or more target cells corresponding to one or more target base stations. Method 2800 also includes receiving 2804 L1 measurements corresponding to one or more target cells from the UE. Method 2800 further includes selecting 2806 a first target cell among the one or more target cells for handover to the UE based on the L1 measurements. Method 2800 further includes generating 2808 an early TA corresponding to a predicted TA for the UE and the first target cell based on the TA information using an early TA prediction model. Method 2800 further includes transmitting 2810 a MAC-CE commanding the UE to perform handover to the first target cell to the UE, wherein the MAC-CE includes the predicted early TA corresponding to the UE and the first target cell.

[0392] In some embodiments, method 2800 further includes transmitting to the UE an applicability condition request corresponding to an applicability condition for selecting an early TA prediction model from one or more early TA prediction models at the RAN; receiving from the UE an applicability condition response indicating a value of the applicability condition; and selecting an early TA prediction model from one or more early TA prediction models based on the value of the applicability condition. In some such embodiments, the applicability condition includes a threshold for the UE's speed, the applicability condition request includes a request for a value for the UE's speed, and the applicability response includes a value for the UE's speed.

[0393] In some implementations of method 2800, the TA information includes a TA value and a timestamp corresponding to the TA value.

[0394] FIG. 29 A method 2900 for a UE according to the implementation discussed herein is illustrated. Method 2900 includes determining, 2902, that an LTM handover to a first target cell has failed based on the expiration of the LTM supervisor time. Method 2900 also includes performing cell selection, 2904, to a second target cell, which is identified by first evaluating one or more actual L1 measurements from high to low, and then evaluating one or more predicted L1 measurements in order of one or more confidence levels corresponding to one or more predicted L1 measurements.

[0395] FIG. 30A method 3000 for a source base station of a RAN according to an embodiment discussed herein is illustrated. Method 3000 includes receiving from a UE 3002 a first or more predicted L3 measurements corresponding to a first target cell of a first target base station. Method 3000 also includes performing a first CHO preparation for the first target cell with the first target base station based on the predicted L3 measurements corresponding to the first target cell 3004. Method 3000 further includes transmitting to the UE 3006 a CHO configuration including a first condition for performing a first handover to the first target cell and a first indication of whether a second or more predicted L3 measurements corresponding to the first target cell can be used to evaluate the first condition. Method 3000 further includes receiving from the UE 3008 a CHO configuration response in response to the CHO configuration.

[0396] In some implementations of method 3000, the CHO configuration includes a confidence level threshold for evaluating the first condition using a second or more predicted L3 measurement.

[0397] In some implementations, method 3000 further includes receiving from the UE a third or more predicted Layer 3 (L3) measurement of a second target cell corresponding to a second target base station; and performing a second CHO preparation for the second target cell with the second target base station based on the third or more predicted L3 measurement corresponding to the second target cell; wherein the CHO configuration further includes a second condition for performing a second handover to the second target cell and a second indication of whether a fourth or more predicted L3 measurement corresponding to the second target cell can be used to evaluate the second condition.

[0398] In some implementations of method 3000, the CHO configuration also includes a second condition for performing a first handover to a first target cell and a second indication of whether a second or more predicted L3 measurements can be used to evaluate the second condition.

[0399] In some implementations of method 3000, the CHO configuration also includes a priority value for the first target cell.

[0400] In some implementations of method 3000, the CHO configuration response includes a proposed change to the list of target cells used by the RAN.

[0401] In some implementations of method 3000, the CHO configuration response includes an update to one or more predicted L3 measurements corresponding to the first target cell.

[0402] In some implementations of method 3000, the CHO configuration response includes a prediction error metric for the first or more predicted L3 measurements.

[0403] In some embodiments of method 3000, the CHO configuration response includes a suggested change to the CHO configuration. In some such embodiments, the suggested change to the CHO configuration includes one or more of a suggested change to the CHO event type, a suggested change to the threshold of the CHO event, and a suggested change to TTT.

[0404] In some implementations of method 3000, the CHO configuration response includes a suggested priority value change for the priority value of the first target cell.

[0405] In some implementations, method 3000 further includes transmitting an update to the CHO configuration to the UE based on information received from the UE in the CHO configuration response.

[0406] In some implementations, method 3000 also includes receiving from the UE a UAI message that includes a proposed change to the list of target cells used by the RAN.

[0407] In some implementations, method 3000 further includes receiving from the UE a UAI message that includes a proposed target cell for performing unconditional handover.

[0408] In some implementations, method 3000 further includes receiving from the UE a UAI message that includes a proposed priority value change for a priority value of a first target cell.

[0409] FIG. 31 A method 3100 for a UE according to the implementation discussed herein is illustrated. Method 3100 includes receiving from a source base station of the network 3102 a first CHO configuration including a first condition for performing a first handover to a first target cell of a first target base station and a first indication of whether the first condition can be evaluated using one or more predicted L3 measurements corresponding to the first target cell. Method 3100 further includes transmitting a CHO configuration response to the network 3104 in response to the first CHO configuration. Method 3100 further includes evaluating 3106 that the first condition of the first CHO configuration has been met. Method 3100 further includes initiating 3108 a first handover to the first target cell in response to the evaluation that the first condition of the first CHO configuration has been met.

[0410] In some implementations, method 3100 further includes generating a second or more predicted L3 measurement corresponding to the first target cell; and transmitting the second or more predicted L3 measurement corresponding to the first target cell to the network before receiving the first CHO configuration from the network.

[0411] In some implementations of method 3100, the CHO configuration response includes an update to a second or more predicted L3 measurement corresponding to the first target cell.

[0412] In some implementations of method 3100, the CHO configuration response includes a prediction error metric for a second or more predicted L3 measurement.

[0413] In some implementations of method 3100, the first CHO configuration includes a confidence level threshold for evaluating the first condition using one or more first predicted L3 measurements.

[0414] In some embodiments of method 3100, the first CHO configuration further includes a second condition for performing a second handover to the second target cell and a second indication of whether the second condition can be evaluated using a second or more predicted L3 measurement corresponding to the second target cell.

[0415] In some embodiments of method 3100, the first CHO configuration further includes a second condition for performing a first handover to a first target cell and a second indication of whether the second condition can be evaluated using one or more of the first predicted L3 measurements.

[0416] In some implementations of method 3100, the first CHO configuration also includes a priority value for the first target cell.

[0417] In some implementations of method 3100, the CHO configuration response includes a suggested change to the list of target cells used by the network.

[0418] In some embodiments of method 3100, the CHO configuration response includes a suggested change to the first CHO configuration. In some such embodiments, the suggested change to the CHO configuration includes one or more of a suggested change to the CHO event type, a suggested change to the threshold of the CHO event, and a suggested change to TTT.

[0419] In some implementations of method 3100, the CHO configuration response includes a suggested priority value change for the priority value of the first target cell.

[0420] In some implementations, the method 3100 also includes receiving an update to the configuration of the first CHO from the network.

[0421] In some implementations, method 3100 also includes transmitting a UAI message to the network that includes a proposed change to the list of target cells used by the RAN.

[0422] In some implementations, method 3100 also includes transmitting to the network a UAI message including a proposed target cell for performing an unconditional handover.

[0423] In some implementations, method 3100 further includes transmitting a UAI message to the network that includes a proposed priority value change for the priority value of the first target cell.

[0424] In some implementations of method 3100, evaluating a first condition that has been met by the first CHO configuration includes at least one of the following: determining one or more actual L3 measurement satisfaction conditions for the first target cell; and determining one or more predicted L3 measurement satisfaction conditions.

[0425] In some implementations of method 3100, evaluating the first condition that the first CHO configuration has been met includes each of the following: determining one or more actual L3 measurement satisfaction conditions for the first target cell; and determining one or more predicted L3 measurement satisfaction conditions.

[0426] In some implementations of method 3100, in response to a comparison between the first priority value of the first target cell and the second priority value of the second target cell of the second target base station that has met the second condition of the second CHO configuration, a first handover to the first target cell is further initiated.

[0427] In some implementations, method 3100 further includes determining that a first handover to the first target cell has failed; and performing cell selection to the second target cell based on a priority value configured for the second target cell at the second target base station.

[0428] In some implementations, method 3100 further includes determining that a first handover to a first target cell has failed; and performing cell selection to a second target cell based on determining that a second CHO configuration for a second handover to a second target cell for a second target base station is satisfied.

[0429] In some implementations, method 3100 further includes determining that a first handover to a first target cell has failed; and performing cell selection to a second target cell based on a confidence level of a second or more predicted L3 measurements corresponding to a second target cell at a second target base station.

[0430] FIG. 32 An example architecture of a wireless communication system 3200 according to the embodiments disclosed herein is illustrated. The following description is provided for an example wireless communication system 3200 operating in conjunction with LTE system standards and / or 5G or NR system standards provided by 3GPP technical specifications.

[0431] like FIG. 32As shown, the wireless communication system 3200 includes UE 3202 and UE 3204 (but any number of UEs may be used). In this example, UE 3202 and UE 3204 are exemplified as smartphones (e.g., handheld touchscreen mobile computing devices capable of connecting to one or more cellular networks), but may also include any mobile or non-mobile computing device configured for wireless communication.

[0432] UE 3202 and UE 3204 can be configured to communicatively couple with RAN 3206. In an implementation, RAN 3206 can be NG-RAN, E-UTRAN, etc. UE 3202 and UE 3204 utilize connections (or channels) with RAN 3206 (shown as connection 3208 and connection 3210, respectively), each connection including a physical communication interface. RAN 3206 may include one or more base stations (such as base station 3212 and base station 3214) implementing connection 3208 and connection 3210.

[0433] In this example, Connection 3208 and Connection 3210 are air interfaces that enable this type of communication coupling and can conform to the RAT used by RAN 3206, such as LTE and / or NR, for example.

[0434] In some implementations, UE 3202 and UE 3204 may also exchange communication data directly via sidelink interface 3216. UE 3204 is shown configured to access an access point (shown as AP 3218) via connection 3220. As an example, connection 3220 may include a local wireless connection, such as a connection conforming to any IEEE 802.11 protocol, wherein AP 3218 may include Wi-Fi. ® Router. In this example, AP 3218 can connect to another network (e.g., the Internet) without using CN 3224.

[0435] In the implementation, UE 3202 and UE 3204 may be configured to communicate with each other or with base station 3212 and / or base station 3214 on a multi-carrier communication channel using orthogonal frequency division multiplexing (OFDM) communication signals according to various communication technologies, such as but not limited to orthogonal frequency division multiple access (OFDMA) communication technology (e.g., for downlink communication) or single-carrier frequency division multiple access (SC-FDMA) communication technology (e.g., for uplink and ProSe or sidelink communication), but the scope of the implementation is not limited in this respect. The OFDM signal may include multiple orthogonal subcarriers.

[0436] In some implementations, all or some of the base stations in base station 3212 or base station 3214 may be implemented as one or more software entities running on a server computer as part of a virtual network. Furthermore, or in other implementations, base station 3212 or base station 3214 may be configured to communicate with each other via interface 3222. In implementations where the wireless communication system 3200 is an LTE system (e.g., when CN 3224 is an EPC), interface 3222 may be an X2 interface. This X2 interface may be defined between two or more base stations (e.g., two or more eNBs, etc.) connected to the EPC and / or between two eNBs connected to the EPC. In implementations where the wireless communication system 3200 is an NR system (e.g., when CN 3224 is a 5GC), interface 3222 may be an Xn interface. The Xn interface is defined between two or more base stations (e.g., two or more gNBs, etc.) connected to the 5GC, between a base station 3212 (e.g., a gNB) connected to the 5GC and an eNB, and / or between two eNBs connected to the 5GC (e.g., CN 3224).

[0437] RAN 3206 is shown communicatively coupled to CN 3224. CN 3224 may include one or more network elements 3226 configured to provide various data and telecommunications services to customers / subscribers (e.g., users of UE 3202 and UE 3204) connected to CN 3224 via RAN 3206. Components of CN 3224 may be implemented in a single physical device or a separate physical device including components for reading and executing instructions from machine-readable or computer-readable media (e.g., non-transitory machine-readable storage media).

[0438] In the implementation scheme, CN 3224 can be an EPC, and RAN 3206 can be connected to CN 3224 via S1 interface 3228. In the implementation scheme, S1 interface 3228 can be divided into two parts: an S1 user plane (S1-U) interface, which carries service data between base station 3212 or base station 3214 and the serving gateway (S-GW); and an S1-MME interface, which is the signaling interface between base station 3212 or base station 3214 and the mobility management entity (MME).

[0439] In the implementation scheme, CN 3224 may be a 5GC, and RAN 3206 may be connected to CN 3224 via NG interface 3228. In the implementation scheme, NG interface 3228 may be divided into two parts: an NG user plane (NG-U) interface, which carries service data between base station 3212 or base station 3214 and user plane function (UPF); and an S1 control plane (NG-C) interface, which is the signaling interface between base station 3212 or base station 3214 and access and mobility management function (AMF).

[0440] Generally, application server 3230 can be an element that provides Internet Protocol (IP) bearer resources (e.g., packet-switched data services) for use with CN 3224. Application server 3230 can also be configured to support one or more communication services (e.g., VoIP sessions, group communication sessions, etc.) for UE 3202 and UE 3204 via CN 3224. Application server 3230 can communicate with CN 3224 via IP communication interface 3232.

[0441] FIG. 33 A system 3300 for executing signaling 3334 between a wireless device 3302 and a network device 3318 according to an embodiment disclosed herein is illustrated. System 3300 may be part of a wireless communication system as described herein. Wireless device 3302 may be, for example, a UE (User Equipment) of a wireless communication system. Network device 3318 may be, for example, a base station (e.g., an eNB or gNB) of a wireless communication system.

[0442] Wireless device 3302 may include one or more processors 3304. Processor 3304 is executable instructions that cause various operations of wireless device 3302 to be performed as described herein. Processor 3304 may include one or more baseband processors, which are implemented using, for example, a central processing unit (CPU), digital signal processor (DSP), application-specific integrated circuit (ASIC), controller, field-programmable gate array (FPGA) device, another hardware device, firmware device, or any combination thereof configured to perform the operations described herein.

[0443] Wireless device 3302 may include memory 3306. Memory 3306 may be a non-transitory computer-readable storage medium that stores instructions 3308, which may include, for example, instructions executed by processor 3304. Instructions 3308 may also be referred to as program code or a computer program. Memory 3306 may also store data used by processor 3304 and results calculated by the processor.

[0444] Wireless device 3302 may include one or more transceivers 3310, which may include radio frequency (RF) transmitter circuitry and / or receiver circuitry, which use antenna 3312 of wireless device 3302 to facilitate signaling (e.g., signaling 3334) to and / or from wireless device 3302 and other devices (e.g., network device 3318) according to the corresponding RAT.

[0445] Wireless device 3302 may include one or more antennas 3312 (e.g., one, two, four or more). In embodiments with multiple antennas 3312, wireless device 3302 may utilize spatial diversity of such multiple antennas 3312 to transmit and / or receive multiple different data streams on the same time and frequency resources. This behavior may be referred to as, for example, multiple-input multiple-output (MIMO) behavior (referring to multiple antennas used at each of the transmitting and receiving devices to implement this aspect). MIMO transmission by wireless device 3302 may be achieved according to pre-decoding (or digital beamforming) applied at wireless device 3302, which multiplexes data streams across antennas 3312 based on known or assumed channel characteristics, such that each data stream is received with appropriate signal strength relative to the others at a desired location in the spatial domain (e.g., the location of the receiver associated with that data stream). Some implementations may use a single-user MIMO (SU-MIMO) approach (where all data streams are directed to a single receiver) and / or a multi-user MIMO (MU-MIMO) approach (where individual data streams may be directed to individual (different) receivers at different locations in the airspace).

[0446] In some implementations with multiple antennas, wireless device 3302 can implement analog beamforming technology, whereby the phase of the signal transmitted by antenna 3312 is relatively adjusted so that the (joint) transmission of antenna 3312 can be directed (this is sometimes referred to as beam control).

[0447] Wireless device 3302 may include one or more interfaces 3314. Interfaces 3314 can be used to provide input to or from wireless device 3302. For example, wireless device 3302 as a UE may include interfaces 3314, such as microphones, speakers, touchscreens, and buttons, to allow users of the UE to make inputs and / or outputs to the UE. Other interfaces of such a UE may consist of transmitters, receivers, and other circuitry that allow communication between the UE and other devices (e.g., in addition to the transceiver 3310 / antenna 3312 already described), and may be based on known protocols (e.g., Wi-Fi). ® and Bluetooth ® (etc.) to perform the operation.

[0448] Wireless device 3302 may include prediction module 3316. Prediction module 3316 may be implemented via hardware, software, or a combination thereof. For example, prediction module 3316 may be implemented as a processor, circuitry, and / or instructions 3308 stored in memory 3306 and executed by processor 3304. In some examples, prediction module 3316 may be integrated within processor 3304 and / or transceiver 3310. For example, prediction module 3316 may be implemented via a combination of software components (e.g., executed by a DSP or general-purpose processor) and hardware components (e.g., logic gates and circuitry) within processor 3304 or transceiver 3310.

[0449] The prediction module 3316 can be used in various aspects of this disclosure, for example, FIGS. 1-19 All aspects. The prediction module 3316 can be configured to enable the wireless device 3302 to perform UE-based functionalities corresponding to L3 beam-level measurement prediction, L1 measurement prediction, TA prediction, and / or the use of CHO, as discussed herein.

[0450] Network device 3318 may include one or more processors 3320. Processor 3320 is executable instructions that cause various operations of network device 3318 to be performed as described herein. Processor 3320 may include one or more baseband processors, which are implemented using, for example, a CPU, DSP, ASIC, controller, FPGA device, another hardware device, firmware device, or any combination thereof configured to perform the operations described herein.

[0451] Network device 3318 may include memory 3322. Memory 3322 may be a non-transitory computer-readable storage medium that stores instructions 3324, which may include, for example, instructions executed by processor 3320. Instructions 3324 may also be referred to as program code or a computer program. Memory 3322 may also store data used by processor 3320 and results calculated by the processor.

[0452] Network device 3318 may include one or more transceivers 3326, which may include RF transmitter circuitry and / or receiver circuitry that uses the antenna 3328 of network device 3318 to facilitate signaling (e.g., signaling 3334) to and / or from network device 3318 and other devices (e.g., wireless device 3302) in accordance with the corresponding RAT.

[0453] Network device 3318 may include one or more antennas 3328 (e.g., one, two, four or more). In embodiments having multiple antennas 3328, network device 3318 may perform MIMO, digital beamforming, analog beamforming, beam control, etc., as described.

[0454] Network device 3318 may include one or more interfaces 3330. Interfaces 3330 can be used to provide input to or output to network device 3318. For example, network device 3318 as a base station may include interfaces 3330 consisting of transmitters, receivers, and other circuitry (e.g., in addition to the transceiver 3326 / antenna 3328 already described), which enable the base station to communicate with other equipment in the core network and / or enable the base station to communicate with external networks, computers, databases, etc., for the purpose of performing operations, management, and maintenance of the base station or other equipment operatively connected to the base station.

[0455] Network device 3318 may include prediction module 3332. Prediction module 3332 may be implemented via hardware, software, or a combination thereof. For example, prediction module 3332 may be implemented as a processor, circuitry, and / or instructions 3324 stored in memory 3322 and executed by processor 3320. In some examples, prediction module 3332 may be integrated within processor 3320 and / or transceiver 3326. For example, prediction module 3332 may be implemented via a combination of software components (e.g., executed by a DSP or general-purpose processor) and hardware components (e.g., logic gates and circuitry) within processor 3320 or transceiver 3326.

[0456] The prediction module 3332 can be used in various aspects of this disclosure, for example, FIGS. 1-19 All aspects. The prediction module 3332 can be configured to enable the network device 3318 to perform base station-based functionalities corresponding to L3 beam-level measurement prediction, L1 measurement prediction, TA prediction, and / or CHO usage, as discussed herein.

[0457] The embodiments contemplated herein include an apparatus comprising components for performing one or more elements of any one or more of methods 2000, 2200, 2300, 2400, 2600, 2700, 2900, and / or 3100. The apparatus may be, for example, a UE (such as wireless device 3302 as a UE, as described herein).

[0458] The embodiments contemplated herein include one or more non-transitory computer-readable media comprising instructions to cause the electronic device to perform one or more elements of any one or more of methods 2000, 2200, 2300, 2400, 2600, 2700, 2900, and / or 3100 when executed by one or more processors of the electronic device. The non-transitory computer-readable medium may, for example, be a memory of the UE (such as memory 3306 of a wireless device 3302 serving as a UE, as described herein).

[0459] The embodiments contemplated herein include an apparatus comprising logic components, modules, or circuitry for performing one or more elements of any one or more of methods 2000, 2200, 2300, 2400, 2600, 2700, 2900, and / or 3100. The apparatus may be, for example, a UE (such as wireless device 3302 as a UE, as described herein).

[0460] The embodiments contemplated herein include an apparatus comprising: one or more processors; and one or more computer-readable media including instructions that, when executed by the one or more processors, cause the one or more processors to perform one or more elements of any one or more of methods 2000, 2200, 2300, 2400, 2600, 2700, 2900, and / or 3100. The apparatus may be, for example, a UE (such as wireless device 3302 as a UE, as described herein).

[0461] The implementation schemes envisioned herein include signals described or associated with one or more elements of any one or more methods such as method 2000, method 2200, method 2300, method 2400, method 2600, method 2700, method 2900 and / or method 3100.

[0462] The embodiments contemplated herein include a computer program or computer program product comprising instructions, wherein execution of the program by a processor will cause the processor to perform one or more elements of any one or more of methods 2000, 2200, 2300, 2400, 2600, 2700, 2900, and / or 3100. The processor may be a processor of the UE (such as processor 3304 as a wireless device 3302 of the UE, as described herein). These instructions may, for example, be located in the processor and / or in the memory of the UE (such as memory 3306 as a wireless device 3302 of the UE, as described herein).

[0463] The embodiments contemplated herein include an apparatus comprising components for performing one or more elements of any one or more of methods 2100, 2500, 2800, and / or 3000. The apparatus may be, for example, a base station (such as network device 3318 as a base station, as described herein).

[0464] The embodiments contemplated herein include one or more non-transitory computer-readable media comprising instructions to cause the electronic device to perform one or more elements of any one or more methods of method 2100, method 2500, method 2800, and / or method 3000 when executed by one or more processors of the electronic device. The non-transitory computer-readable medium may be, for example, the memory of a base station (such as memory 3322 of a network device 3318 serving as a base station, as described herein).

[0465] The embodiments contemplated herein include an apparatus comprising logic components, modules, or circuitry for performing one or more elements of any one or more of methods 2100, 2500, 2800, and / or 3000. The apparatus may be, for example, an apparatus for a base station (such as network device 3318 as a base station, as described herein).

[0466] The embodiments contemplated herein include an apparatus comprising: one or more processors and one or more computer-readable media including instructions that, when executed by the one or more processors, cause the one or more processors to perform one or more elements of any one or more methods of method 2100, method 2500, method 2800, and / or method 3000. The apparatus may be, for example, an apparatus for a base station (such as network device 3318 as a base station, as described herein).

[0467] The implementation scheme envisioned herein includes a signal as described in or associated with one or more elements of any one or more methods of method 2100, method 2500, method 2800, and / or method 3000.

[0468] The embodiments contemplated herein include a computer program or computer program product comprising instructions, wherein execution by a processing element causes the processing element to perform one or more elements of any one or more of methods 2100, 2500, 2800, and / or 3000. The processor may be a processor of a base station (such as processor 3320 of network device 3318 as a base station, as described herein). These instructions may, for example, be located in the processor and / or in the memory of the base station (such as memory 3322 of network device 3318 as a base station, as described herein).

[0469] For one or more embodiments, at least one of the components illustrated in one or more of the foregoing figures may be configured to perform one or more operations, techniques, processes, and / or methods as described herein. For example, a baseband processor as described herein in conjunction with one or more of the foregoing figures may be configured to operate according to one or more of the examples illustrated herein. Similarly, circuitry associated with a UE, base station, network element, etc., as described above in conjunction with one or more of the foregoing figures may be configured to operate according to one or more of the examples illustrated herein.

[0470] Unless otherwise expressly stated, any of the embodiments described above may be combined with any other embodiment (or combination of embodiments). The foregoing description of one or more specific embodiments provides illustrative and descriptive information, but is not intended to be exhaustive or to limit the scope of the embodiments to the precise form disclosed. In light of the teachings above, modifications and variations are possible, or modifications and variations may be derived from practice with various embodiments.

[0471] Implementations and specific embodiments of the systems and methods described herein may include various operations embodied in machine-executable instructions to be executed by a computer system. The computer system may include one or more general-purpose or special-purpose computers (or other electronic devices). The computer system may include hardware components, including specific logical parts for performing the operations; or may include a combination of hardware, software, and / or firmware.

[0472] It should be recognized that the systems described herein include descriptions of specific implementations. These implementations may be combined into a single system, partially integrated into other systems, divided into multiple systems, or otherwise partitioned or combined. Furthermore, it is conceivable to use parameters, attributes, aspects, etc., of one implementation in one implementation. For clarity, these parameters, attributes, aspects, etc., are described only in one or more implementations, and it should be recognized that, unless expressly stated herein, these parameters, attributes, aspects, etc., may be combined with or substituted for parameters, attributes, aspects, etc., of another implementation.

[0473] As is widely recognized, the use of personally identifiable information should comply with privacy policies and practices that are generally accepted to meet or exceed industry or governmental requirements for protecting user privacy. Specifically, personally identifiable information data should be managed and processed to minimize the risk of unintentional or unauthorized access or use, and the nature of authorized use should be clearly explained to users.

[0474] Although the foregoing has been described in considerable detail for clarity, it will be apparent that certain changes and modifications can be made without departing from the principles of the invention. It should be noted that there are many alternative ways to implement both the processes and apparatus described herein. Therefore, embodiments of the invention should be considered illustrative rather than restrictive, and this description is not limited to the details given herein, but can be modified within the scope of the appended claims and their equivalents.

Claims

1. A method for a user equipment (UE), the method comprising: Transmit a notification message to the network that identifies one or more measurement prediction models at the UE; Receive from the network an activation message for a first measurement prediction model available for use among the one or more measurement prediction models at the UE; Generate one or more actual measurements of one or more reference signals received at the UE from the cell of the network; The first measurement prediction model is used to generate one or more predicted measurements based on the one or more actual measurements; as well as A first measurement report, including the one or more predicted measurements, is transmitted to the network.

2. The method of claim 1, wherein the one or more predicted measurements include one or more predicted Layer 3 (L3) cell-level measurements.

3. The method according to claim 2, wherein: The one or more actual measurements include one or more actual L3 cell-level measurements for the one or more reference signals received at the UE; and The one or more predicted L3 cell-level measurements are generated using the first measurement prediction model by providing the one or more actual L3 cell-level measurements to the first measurement prediction model.

4. The method according to claim 2, wherein: The one or more actual measurements include one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and The one or more predicted L3 cell-level measurements are generated using the first measurement prediction model through the following operations: Provide the first measurement prediction model with the one or more actual L1 beam-level measurements to generate one or more predicted L1 beam-level measurements; and Linear averaging and L3 filtering are performed on the one or more actual L1 beamlevel measurements and the one or more predicted L1 beamlevel measurements.

5. The method of claim 4, wherein the L3 filter coefficients used for the L3 filtering are generated by the measurement prediction model.

6. The method according to claim 2, wherein: The one or more actual measurements include one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and The one or more predicted L3 cell-level measurements are generated using the first measurement prediction model through the following operations: Perform linear averaging on the one or more actual L1 beam-level measurements; as well as The first measurement prediction model is provided with one or more actual linear average results of the linear average and a configured set of L3 filter coefficients.

7. The method according to claim 2, wherein: The one or more actual measurements include one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and The one or more predicted L3 cell-level measurements are generated using the first measurement prediction model by providing the first measurement prediction model with a set of L3 filter coefficients of the one or more actual L1 beam-level measurements and configurations.

8. The method according to claim 2, wherein: The one or more predicted L3 cell-level measurements are for the neighboring cells of the cell; and The generation of one or more predicted L3 cell-level measurements using the first measurement prediction model is also based on the correlation information of the neighboring cells.

9. The method of claim 2, further comprising receiving configuration information identifying the cell from the network, wherein the one or more predicted L3 cell-level measurements are for the cell.

10. The method of claim 2, further comprising receiving configuration information identifying a frequency from the network, wherein the one or more predicted L3 cell-level measurements are for the frequency.

11. The method of claim 2, further comprising receiving from the network configuration information identifying conditions for generating the one or more predicted L3 cell-level measurements, wherein the one or more predicted L3 cell-level measurements are generated in response to determining at the UE that the conditions have been met.

12. The method of claim 11, wherein the condition includes whether the previous L3 measurement is less than a threshold.

13. The method of claim 11, wherein the condition includes determining that the one or more predicted L3 cell-level measurements will correspond to inter-frequency measurements.

14. The method of claim 2, further comprising receiving configuration information identifying the cell from the network, wherein the UE selects to generate the one or more predicted L3 cell-level measurements based on the identifier of the cell in the configuration information.

15. The method of claim 1, wherein the one or more predicted measurements include one or more predicted layer 3 (L3) beam-level measurements.

16. The method of claim 15, wherein: The one or more actual measurements include one or more actual L3 beam-level measurements of the one or more reference signals received at the UE; and The one or more predicted L3 beam level measurements are generated using the first measurement prediction model by providing the one or more actual L3 beam level measurements to the first measurement prediction model.

17. The method of claim 15, wherein: The one or more actual measurements include one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and The one or more predicted L3 beam-level measurements are generated using the first measurement prediction model through the following operations: Provide the first measurement prediction model with the one or more actual L1 beam-level measurements to generate one or more predicted L1 beam-level measurements; and L3 filtering is performed on the one or more actual L1 beam level measurements and the one or more predicted L1 beam level measurements.

18. The method of claim 15, wherein: The one or more actual measurements include one or more actual L1 beam-level measurements of the one or more reference signals received at the UE; and The one or more predicted L3 beamlevel measurements are generated using the first measurement prediction model by providing the one or more actual L1 beamlevel measurements to the first measurement prediction model.

19. The method of claim 15, wherein: The one or more reference signals are received on one or more beams; The one or more predicted L3 beam-level measurements are for neighboring beams of the one or more beams, which are not part of the one or more beams. and The generation of one or more predicted L3 beam-level measurements using the first measurement prediction model is also based on statistical information about the channel between the UE and the cell.

20. The method of claim 15, further comprising receiving configuration information identifying a beam from the network, wherein the one or more predicted L3 beam-level measurements are for the beam.

21. The method of claim 15, further comprising receiving from the network configuration information identifying conditions for generating the one or more predicted L3 beamlevel measurements, wherein the one or more predicted L3 beamlevel measurements are generated in response to determining at the UE that the conditions have been met.

22. The method of claim 21, wherein the condition includes whether the previous L3 measurement is less than a threshold.

23. The method of claim 21, wherein the condition includes determining that the one or more predicted L3 beam-level measurements will correspond to inter-frequency measurements.

24. The method of claim 15, further comprising receiving configuration information identifying a beam from the network, wherein the UE selects, based on the identifier of the beam in the configuration information, to include predicted L3 cell-level measurements for the beam in the one or more predicted L3 beam-level measurements.

25. The method of claim 1, wherein the one or more predicted measurements include one or more predicted layer 1 (L1) beam-level measurements.

26. The method of claim 25, wherein: The one or more actual measurements include one or more actual L1 beam-level measurements for the one or more reference signals received at the UE; and The one or more predicted L1 beam level measurements are generated using the first measurement prediction model by providing the one or more actual L1 beam level measurements to the first measurement prediction model.

27. The method of claim 25, wherein: The one or more reference signals are received on one or more beams; The one or more predicted L1 beam-level measurements are for neighboring beams of the one or more beams, which are not part of the one or more beams.

28. The method of claim 25, further comprising receiving configuration information identifying a beam from the network, wherein the one or more predicted L1 beam-level measurements are for the beam.

29. The method of claim 25, further comprising receiving from the network configuration information identifying conditions for generating the one or more predicted L1 beam-level measurements, wherein the one or more predicted L1 beam-level measurements are generated in response to determining at the UE that the conditions have been met.

30. The method of claim 29, wherein the condition includes whether a previous L1 measurement is less than a threshold.

31. The method of claim 29, wherein the condition includes determining that the one or more predicted L1 beam-level measurements will correspond to inter-frequency measurements.

32. The method of claim 25, further comprising receiving configuration information identifying a beam from the network, wherein the UE selects to include predicted L1 cell-level measurements for the beam in the one or more predicted L1 beam-level measurements based on the identifier of the beam in the configuration information.

33. The method according to claim 1, further comprising: Receive configuration information from the network indicating the predicted measurement periodicity and the actual measurement periodicity; as well as A second measurement report, including one or more of the actual measurements, is periodically transmitted to the UE based on the actual measurements. This includes the first measurement report of the one or more predicted measurements being transmitted periodically based on the predicted measurements.

34. The method of claim 1, wherein the transmission of the first measurement report, which includes the one or more predicted measurements, to the network is triggered by the one or more predicted measurements.

35. The method of claim 1, wherein the transmission of the first measurement report, which includes the one or more predicted measurements, to the network is triggered by the actual measurement of the one or more reference signals.

36. The method of claim 1, wherein the first measurement report further includes an indication that the one or more predicted measurements are predictive measurements.

37. The method of claim 1, wherein the first measurement report further includes the one or more actual measurements of the one or more reference signals.

38. The method of claim 1, wherein the notification message further comprises one or more of the following: Predicted optimal L3 filter coefficients; The best type of event to predict in a measurement report; Optimal trigger time (TTT) for the predicted measurement report event; The optimal threshold for predicting the measured reporting events; Recommended cell for actual measurement; A suggested beam for the first actual measurement in one or more of the actual measurements; and Recommended T304 timer values.

39. The method of claim 1, further comprising transmitting to the network auxiliary information including one or more of the following: Geometric structure of nearby base station deployment; The first statistic corresponding to time correlation; The second statistic corresponds to the inter-small interval correlation; as well as The third statistic corresponds to the inter-beam correlation.

40. A method for a radio access network (RAN), the method comprising: Receive measurement prediction models from user equipment (UE); Transmit one or more reference signals to the UE; Actual measurement of receiving the one or more reference signals from the UE; as well as The measurement prediction model is used to generate one or more predicted measurements based on the actual measurements of the one or more reference signals; The measurement prediction model mentioned above is one of the following: Layer 3 (L3) cell-level measurement prediction model; L3 beam-level measurement prediction model; Layer 1 (L1) beam-level measurement prediction model.

41. The method of claim 40, further comprising transmitting to the UE configuration information to be used by the UE to retrain the measurement prediction model.

42. The method of claim 40, further comprising receiving from the UE a timestamp for generating the actual measurement.

43. The method of claim 40, further comprising receiving from the UE the location of the UE and the UE the movement orientation.

44. The method of claim 40, further comprising receiving from the UE a change in the UE's mobility orientation.

45. A method for a user equipment (UE), the method comprising: One or more predicted measurements are generated using a measurement prediction model based on a first reference signal received at the UE from a cell in the network. One or more actual measurements corresponding to the one or more predicted measurements are generated by measuring a second reference signal received from the cell at the UE; The confidence level is calculated using the one or more predicted measurements and the one or more actual measurements; as well as Report the confidence level to the network.

46. ​​The method of claim 45, wherein calculating the confidence level includes determining the mean square error (MSE) between the predicted measurement and the actual measurement.

47. The method of claim 45, further comprising receiving from the network an instruction to stop using the measurement prediction model.

48. The method of claim 45, further comprising reporting to the network a first timestamp of generating the predicted measurement and a second timestamp of generating the actual measurement.

49. The method of claim 45, further comprising reporting the location of the UE and the movement orientation of the UE to the network.

50. A method for a user equipment (UE), the method comprising: One or more predicted measurements are generated using a measurement prediction model based on a first reference signal received at the UE from a cell in the network. One or more actual measurements corresponding to the one or more predicted measurements are generated by measuring a second reference signal received from the cell at the UE; The confidence level is calculated using the one or more predicted measurements and the one or more actual measurements; as well as Based on the stated confidence level, the use of the measurement prediction model is discontinued.

51. The method of claim 50, wherein calculating the confidence level includes determining the mean square error (MSE) between the predicted measurement and the actual measurement.

52. An apparatus comprising components for performing the method according to any one of claims 1 to 51.

53. A computer-readable medium comprising instructions to cause the electronic device to perform the method according to any one of claims 1 to 51 when the instructions are executed by one or more processors of the electronic device.

54. An apparatus comprising a logic component, module, or circuit for performing the method according to any one of claims 1 to 51.

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